{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "physical-parade",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Using CellTypist for multi-label classification\n",
    "This notebook showcases the multi-label classification for scRNA-seq query data using either the built-in CellTypist models or the user-trained custom models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acknowledged-worship",
   "metadata": {},
   "source": [
    "Only the main steps and key parameters are introduced in this notebook. Refer to detailed [Usage](https://github.com/Teichlab/celltypist#usage) if you want to learn more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frozen-latex",
   "metadata": {},
   "source": [
    "## About multi-label cell type classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strong-parade",
   "metadata": {},
   "source": [
    "An ideal CellTypist model is supposed to be trained from a reference atlas with a comprehensive cell type repertoire. For the built-in models, we have collected a large number of cell types; yet, the presence of unexpected (e.g., low-quality or novel cell types) and ambiguous cell states (e.g., doublets) in the query data is beyond the prediction that CellTypist can achieve with a 'find-a-best-match' mode. To overcome this, CellTypist provides the option of multi-label cell type classification, which assigns 0 (i.e., unassigned), 1, or >=2 cell type labels to each query cell."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arabic-chosen",
   "metadata": {},
   "source": [
    "## Install CellTypist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "black-rental",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting celltypist\n",
      "  Using cached celltypist-0.1.9-py3-none-any.whl (5.0 MB)\n",
      "Requirement already satisfied: scikit-learn>=0.24.1 in /opt/conda/lib/python3.8/site-packages (from celltypist) (0.24.1)\n",
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      "Requirement already satisfied: numpy>=1.19.0 in /opt/conda/lib/python3.8/site-packages (from celltypist) (1.20.1)\n",
      "Requirement already satisfied: openpyxl>=3.0.4 in /opt/conda/lib/python3.8/site-packages (from celltypist) (3.0.7)\n",
      "Requirement already satisfied: requests>=2.23.0 in /opt/conda/lib/python3.8/site-packages (from celltypist) (2.25.1)\n",
      "Requirement already satisfied: pandas>=1.0.5 in /opt/conda/lib/python3.8/site-packages (from celltypist) (1.2.3)\n",
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      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=1.0.5->celltypist) (1.15.0)\n",
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      "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests>=2.23.0->celltypist) (4.0.0)\n",
      "Requirement already satisfied: umap-learn>=0.3.10 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.4.6)\n",
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      "Requirement already satisfied: h5py>=2.10.0 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (3.1.0)\n",
      "Requirement already satisfied: anndata>=0.7.4 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.7.5)\n",
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      "Requirement already satisfied: matplotlib>=3.1.2 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (3.3.4)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (20.9)\n",
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      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (8.1.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (1.3.1)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.8/site-packages (from networkx>=2.3->scanpy>=1.7.0->celltypist) (4.4.2)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.8/site-packages (from numba>=0.41.0->scanpy>=1.7.0->celltypist) (49.6.0.post20210108)\n",
      "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /opt/conda/lib/python3.8/site-packages (from numba>=0.41.0->scanpy>=1.7.0->celltypist) (0.34.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn>=0.24.1->celltypist) (2.1.0)\n",
      "Requirement already satisfied: get-version>=2.0.4 in /opt/conda/lib/python3.8/site-packages (from legacy-api-wrap->scanpy>=1.7.0->celltypist) (2.1)\n",
      "Requirement already satisfied: stdlib-list in /opt/conda/lib/python3.8/site-packages (from sinfo->scanpy>=1.7.0->celltypist) (0.7.0)\n",
      "Requirement already satisfied: numexpr>=2.6.2 in /opt/conda/lib/python3.8/site-packages (from tables->scanpy>=1.7.0->celltypist) (2.7.3)\n",
      "Installing collected packages: celltypist\n",
      "Successfully installed celltypist-0.1.9\n"
     ]
    }
   ],
   "source": [
    "!pip install celltypist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "faced-contributor",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "micro-treatment",
   "metadata": {},
   "outputs": [],
   "source": [
    "import celltypist\n",
    "from celltypist import models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "communist-accused",
   "metadata": {},
   "source": [
    "## Download a scRNA-seq dataset of 500 immune cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bulgarian-partner",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f2f68a7a2bf45efa6093bdd73ac1774",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/9.41M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_500 = sc.read('celltypist_demo_folder/demo_500_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_500_cells.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broken-stage",
   "metadata": {
    "tags": []
   },
   "source": [
    "This dataset includes 500 cells and 18,950 genes collected from different studies, thereby showing the practical applicability of CellTypist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "saved-claim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 18950)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_500.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "young-garage",
   "metadata": {
    "tags": []
   },
   "source": [
    "The expression matrix (`adata_500.X`) is pre-processed (and required) as log1p normalised expression to 10,000 counts per cell (this matrix can be alternatively stashed in `.raw.X`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dimensional-oklahoma",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[10000.   ],\n",
       "        [10000.001],\n",
       "        [ 9999.999],\n",
       "        [10000.001],\n",
       "        [10000.003],\n",
       "        [ 9999.999],\n",
       "        [10000.   ],\n",
       "        [10000.002],\n",
       "        [10000.   ],\n",
       "        [ 9999.999]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_500.X.expm1().sum(axis = 1)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intensive-building",
   "metadata": {},
   "source": [
    "Some pre-assigned cell type labels are also in the data, which will be compared to the predicted labels from CellTypist later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "powerful-london",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell496</th>\n",
       "      <td>Macro_pDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell497</th>\n",
       "      <td>Macro_pDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell498</th>\n",
       "      <td>Macro_pDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell499</th>\n",
       "      <td>Macro_pDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell500</th>\n",
       "      <td>Macro_pDC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cell_type\n",
       "cell1    Plasma cells\n",
       "cell2    Plasma cells\n",
       "cell3    Plasma cells\n",
       "cell4    Plasma cells\n",
       "cell5    Plasma cells\n",
       "...               ...\n",
       "cell496     Macro_pDC\n",
       "cell497     Macro_pDC\n",
       "cell498     Macro_pDC\n",
       "cell499     Macro_pDC\n",
       "cell500     Macro_pDC\n",
       "\n",
       "[500 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_500.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "danish-adrian",
   "metadata": {},
   "source": [
    "Among the 12 cell types in this data, 10 are shared with the CellTypist built-in models. For the remaining two, `Microglia` is a novel cell type not covered by CellTypist (currently our models do not involve the brain), and `Macro_pDC` is a cell type *in silico* generated by blending the expression of macrophages with plasmacytoid dendritic cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "excited-heating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.dotplot(adata_500, ['CX3CR1', 'C1QC', 'LILRA4'], groupby = 'cell_type', swap_axes = True, standard_scale = 'var', figsize = [12, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "military-lucas",
   "metadata": {},
   "source": [
    "## Inspect the CellTypist built-in models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tribal-garlic",
   "metadata": {},
   "source": [
    "Download the latest CellTypist models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "declared-theta",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "📜 Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json\n",
      "📚 Total models in list: 7\n",
      "📂 Storing models in /home/jovyan/.celltypist/data/models\n",
      "💾 Downloading model [1/7]: Immune_All_Low.pkl\n",
      "💾 Downloading model [2/7]: Immune_All_High.pkl\n",
      "💾 Downloading model [3/7]: Immune_All_PIP.pkl\n",
      "💾 Downloading model [4/7]: Immune_All_AddPIP.pkl\n",
      "💾 Downloading model [5/7]: Cells_Intestinal_Tract.pkl\n",
      "💾 Downloading model [6/7]: Cells_Lung_Airway.pkl\n",
      "💾 Downloading model [7/7]: Nuclei_Lung_Airway.pkl\n"
     ]
    }
   ],
   "source": [
    "# Enabling `force_update = True` will overwrite existing (old) models.\n",
    "models.download_models(force_update = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tender-herald",
   "metadata": {},
   "source": [
    "All models are stored in `models.models_path`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "immediate-yugoslavia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/.celltypist/data/models'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.models_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dutch-christianity",
   "metadata": {},
   "source": [
    "Get an overview of the models and what they represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "intelligent-dallas",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "👉 Detailed model information can be found at `https://www.celltypist.org/models`\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immune_All_Low.pkl</td>\n",
       "      <td>immune sub-populations combined from 20 tissue...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immune_All_High.pkl</td>\n",
       "      <td>immune populations combined from 20 tissues of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immune_All_PIP.pkl</td>\n",
       "      <td>immune cell types combined from 16 adult human...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immune_All_AddPIP.pkl</td>\n",
       "      <td>immune cell types combined from &gt;20 human tiss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cells_Intestinal_Tract.pkl</td>\n",
       "      <td>intestinal cells from fetal, pediatric and adu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cells_Lung_Airway.pkl</td>\n",
       "      <td>cell populations from scRNA-seq of five locati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nuclei_Lung_Airway.pkl</td>\n",
       "      <td>cell populations from snRNA-seq of five locati...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        model  \\\n",
       "0          Immune_All_Low.pkl   \n",
       "1         Immune_All_High.pkl   \n",
       "2          Immune_All_PIP.pkl   \n",
       "3       Immune_All_AddPIP.pkl   \n",
       "4  Cells_Intestinal_Tract.pkl   \n",
       "5       Cells_Lung_Airway.pkl   \n",
       "6      Nuclei_Lung_Airway.pkl   \n",
       "\n",
       "                                         description  \n",
       "0  immune sub-populations combined from 20 tissue...  \n",
       "1  immune populations combined from 20 tissues of...  \n",
       "2  immune cell types combined from 16 adult human...  \n",
       "3  immune cell types combined from >20 human tiss...  \n",
       "4  intestinal cells from fetal, pediatric and adu...  \n",
       "5  cell populations from scRNA-seq of five locati...  \n",
       "6  cell populations from snRNA-seq of five locati...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.models_description()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intended-glance",
   "metadata": {},
   "source": [
    "Choose the model you want to employ, for example, the model with all tissues combined containing low-hierarchy (high-resolution) cell types/subtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "vulnerable-devices",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indeed, the `model` argument defaults to `Immune_All_Low.pkl`.\n",
    "model = models.Model.load(model = 'Immune_All_Low.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-discipline",
   "metadata": {},
   "source": [
    "This model contains 91 cell states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "blocked-nerve",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B cells', 'CD16+ NK cells', 'CD16- NK cells', 'CD8a/a',\n",
       "       'CD8a/b(entry)', 'CMP', 'Classical monocytes', 'Cycling B cells',\n",
       "       'Cycling DCs', 'Cycling NK cells', 'Cycling T cells',\n",
       "       'Cycling gamma-delta T cells', 'Cycling monocytes',\n",
       "       'Cytotoxic T cells', 'DC', 'DC precursor', 'DC1', 'DC2', 'DC3',\n",
       "       'Double-negative thymocytes', 'Double-positive thymocytes', 'ELP',\n",
       "       'ETP', 'Early MK', 'Early erythroid', 'Early lymphoid/T lymphoid',\n",
       "       'Endothelial cells', 'Epithelial cells', 'Erythrocytes',\n",
       "       'Fibroblasts', 'Follicular B cells', 'Follicular helper T cells',\n",
       "       'GMP', 'Germinal center B cells', 'Granulocytes', 'HSC/MPP',\n",
       "       'Helper T cells', 'Hofbauer cells', 'ILC', 'ILC precursor', 'ILC1',\n",
       "       'ILC2', 'ILC3', 'Immature B cells', 'Kidney-resident macrophages',\n",
       "       'Kupffer cells', 'Late erythroid', 'MAIT cells', 'MEMP', 'MNP',\n",
       "       'Macrophages', 'Mast cells', 'Megakaryocyte precursor',\n",
       "       'Megakaryocyte-erythroid-mast cell progenitor',\n",
       "       'Megakaryocytes/platelets', 'Memory B cells',\n",
       "       'Memory CD4+ cytotoxic T cells', 'Mid erythroid', 'Migratory DCs',\n",
       "       'Mono-mac', 'Monocyte precursor', 'Monocytes', 'Myelocytes',\n",
       "       'NK cells', 'NKT cells', 'Naive B cells',\n",
       "       'Neutrophil-myeloid progenitor', 'Neutrophils',\n",
       "       'Non-classical monocytes', 'Plasma cells', 'Pre-B cells',\n",
       "       'Pre-pro-B cells', 'Pro-B cells', 'Promyelocytes',\n",
       "       'Regulatory T cells', 'T cells', 'T(agonist)',\n",
       "       'Tcm/Naive cytotoxic T cells', 'Tcm/Naive helper T cells',\n",
       "       'Tem/Effector cytotoxic T cells', 'Tem/Effector helper T cells',\n",
       "       'Tem/Effector helper T cells PD1+', 'Transitional B cells',\n",
       "       'Transitional DC', 'Transitional NK', 'Treg(diff)',\n",
       "       'Type 1 helper T cells', 'Type 17 helper T cells',\n",
       "       'gamma-delta T cells', 'pDC', 'pDC precursor'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cell_types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intimate-vector",
   "metadata": {
    "tags": []
   },
   "source": [
    "Some model meta-information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "competitive-museum",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date': '2021-10-27 15:20:55.163288',\n",
       " 'details': 'immune sub-populations combined from 20 tissues of 19 studies',\n",
       " 'url': 'https://celltypist.cog.sanger.ac.uk/models/Pan_Immune_CellTypist/v1/Immune_All_Low.pkl',\n",
       " 'source': 'https://doi.org/10.1101/2021.04.28.441762',\n",
       " 'version': 'v1',\n",
       " 'number_celltypes': 91}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stainless-economy",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Single-label classification by finding the best match in the model\n",
    "In this section, we show the procedure of finding the most likely cell type labels from built-in models for the query dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accepted-testimony",
   "metadata": {
    "tags": []
   },
   "source": [
    "We use the default mode (`mode = 'best match'`) to transfer cell type labels from the model to the query dataset. With this mode on, each query cell is predicted into the cell type with the largest score/probability among all possible cell types in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "direct-campaign",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🔬 Input data has 500 cells and 18950 genes\n",
      "🔗 Matching reference genes in the model\n",
      "🧬 3278 features used for prediction\n",
      "⚖️ Scaling input data\n",
      "🖋️ Predicting labels\n",
      "✅ Prediction done!\n",
      "👀 Can not detect a neighborhood graph, construct one before the over-clustering\n",
      "⛓️ Over-clustering input data with resolution set to 5\n",
      "🗳️ Majority voting the predictions\n",
      "✅ Majority voting done!\n"
     ]
    }
   ],
   "source": [
    "# Not run; predict cell identities using this loaded model.\n",
    "#predictions = celltypist.annotate(adata_500, model = model, majority_voting = True, mode = 'best match')\n",
    "# Alternatively, just specify the model name (recommended as this ensures the model is intact every time it is loaded).\n",
    "predictions = celltypist.annotate(adata_500, model = 'Immune_All_Low.pkl', majority_voting = True, mode = 'best match')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fixed-reaction",
   "metadata": {},
   "source": [
    "By default (`majority_voting = False`), CellTypist will infer the identity of each query cell independently. This leads to raw predicted cell type labels, and usually finishes within seconds or minutes depending on the size of the query data. You can also turn on the majority-voting classifier (`majority_voting = True`), which refines cell identities within local subclusters after an over-clustering approach at the cost of increased runtime."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-taxation",
   "metadata": {
    "tags": []
   },
   "source": [
    "The results include both predicted cell type labels (`predicted_labels`), over-clustering result (`over_clustering`), and predicted labels after majority voting in local subclusters (`majority_voting`). Note in the `predicted_labels`, each query cell gets its inferred label by choosing the most probable cell type among all possible cell types in the given model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "working-stock",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_labels</th>\n",
       "      <th>over_clustering</th>\n",
       "      <th>majority_voting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>8</td>\n",
       "      <td>Follicular B cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>6</td>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>7</td>\n",
       "      <td>Plasma cells</td>\n",
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       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>6</td>\n",
       "      <td>Plasma cells</td>\n",
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       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>6</td>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>cell496</th>\n",
       "      <td>pDC</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell497</th>\n",
       "      <td>Macrophages</td>\n",
       "      <td>17</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell498</th>\n",
       "      <td>Macrophages</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell499</th>\n",
       "      <td>Macrophages</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell500</th>\n",
       "      <td>pDC</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        predicted_labels over_clustering     majority_voting\n",
       "cell1       Plasma cells               8  Follicular B cells\n",
       "cell2       Plasma cells               6        Plasma cells\n",
       "cell3       Plasma cells               7        Plasma cells\n",
       "cell4       Plasma cells               6        Plasma cells\n",
       "cell5       Plasma cells               6        Plasma cells\n",
       "...                  ...             ...                 ...\n",
       "cell496              pDC              10         Macrophages\n",
       "cell497      Macrophages              17         Macrophages\n",
       "cell498      Macrophages              10         Macrophages\n",
       "cell499      Macrophages              10         Macrophages\n",
       "cell500              pDC              10         Macrophages\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.predicted_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "upset-herald",
   "metadata": {},
   "source": [
    "Transform the prediction result into an `AnnData`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "documented-range",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get an `AnnData` with predicted labels embedded into the cell metadata columns.\n",
    "adata = predictions.to_adata()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affiliated-museum",
   "metadata": {},
   "source": [
    "Compared to `adata_500`, the new `adata` has additional prediction information in `adata.obs` (`predicted_labels`, `over_clustering`, `majority_voting` and `conf_score`). Of note, all these columns can be prefixed with a specific string by setting `prefix` in `to_adata`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "temporal-motion",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "      <th>predicted_labels</th>\n",
       "      <th>over_clustering</th>\n",
       "      <th>majority_voting</th>\n",
       "      <th>conf_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>8</td>\n",
       "      <td>Follicular B cells</td>\n",
       "      <td>0.999847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>6</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.999676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>7</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.999786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>6</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.994295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>6</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.998509</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>cell496</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>pDC</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.849160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell497</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>17</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.986548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell498</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.996593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell499</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.985894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell500</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>pDC</td>\n",
       "      <td>10</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.982121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cell_type predicted_labels over_clustering     majority_voting  \\\n",
       "cell1    Plasma cells     Plasma cells               8  Follicular B cells   \n",
       "cell2    Plasma cells     Plasma cells               6        Plasma cells   \n",
       "cell3    Plasma cells     Plasma cells               7        Plasma cells   \n",
       "cell4    Plasma cells     Plasma cells               6        Plasma cells   \n",
       "cell5    Plasma cells     Plasma cells               6        Plasma cells   \n",
       "...               ...              ...             ...                 ...   \n",
       "cell496     Macro_pDC              pDC              10         Macrophages   \n",
       "cell497     Macro_pDC      Macrophages              17         Macrophages   \n",
       "cell498     Macro_pDC      Macrophages              10         Macrophages   \n",
       "cell499     Macro_pDC      Macrophages              10         Macrophages   \n",
       "cell500     Macro_pDC              pDC              10         Macrophages   \n",
       "\n",
       "         conf_score  \n",
       "cell1      0.999847  \n",
       "cell2      0.999676  \n",
       "cell3      0.999786  \n",
       "cell4      0.994295  \n",
       "cell5      0.998509  \n",
       "...             ...  \n",
       "cell496    0.849160  \n",
       "cell497    0.986548  \n",
       "cell498    0.996593  \n",
       "cell499    0.985894  \n",
       "cell500    0.982121  \n",
       "\n",
       "[500 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respiratory-miniature",
   "metadata": {},
   "source": [
    "In addition to this meta information added, the neighborhood graph constructed during over-clustering is also stored in the `adata` \n",
    "(If a pre-calculated neighborhood graph is already present in the `AnnData`, this graph construction step will be skipped).  \n",
    "This graph can be used to derive the cell embeddings, such as the UMAP coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "basic-wallet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the UMAP or any cell embeddings are already available in the `AnnData`, skip this command.\n",
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saved-vehicle",
   "metadata": {},
   "source": [
    "Visualise the prediction results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "continental-aside",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 989.28x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['cell_type', 'majority_voting'], legend_loc = 'on data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aware-subsection",
   "metadata": {
    "tags": []
   },
   "source": [
    "As the images show, with the default mode, `Microglia` is predicted as a mixture of cell types, and `Macro_pDC` is predicted as `Macrophages`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "seasonal-forth",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>majority_voting</th>\n",
       "      <th>DC1</th>\n",
       "      <th>Endothelial cells</th>\n",
       "      <th>Epithelial cells</th>\n",
       "      <th>Fibroblasts</th>\n",
       "      <th>Follicular B cells</th>\n",
       "      <th>Kupffer cells</th>\n",
       "      <th>Macrophages</th>\n",
       "      <th>Mast cells</th>\n",
       "      <th>Neutrophil-myeloid progenitor</th>\n",
       "      <th>Plasma cells</th>\n",
       "      <th>gamma-delta T cells</th>\n",
       "      <th>pDC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Microglia</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macro_pDC</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "majority_voting  DC1  Endothelial cells  Epithelial cells  Fibroblasts  \\\n",
       "cell_type                                                                \n",
       "Microglia         13                  1                14           18   \n",
       "Macro_pDC          0                  0                 1            0   \n",
       "\n",
       "majority_voting  Follicular B cells  Kupffer cells  Macrophages  Mast cells  \\\n",
       "cell_type                                                                     \n",
       "Microglia                         0              0            9           0   \n",
       "Macro_pDC                         0              0           39           0   \n",
       "\n",
       "majority_voting  Neutrophil-myeloid progenitor  Plasma cells  \\\n",
       "cell_type                                                      \n",
       "Microglia                                    1             0   \n",
       "Macro_pDC                                    0             0   \n",
       "\n",
       "majority_voting  gamma-delta T cells  pDC  \n",
       "cell_type                                  \n",
       "Microglia                          0    4  \n",
       "Macro_pDC                          0    0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(adata.obs.cell_type, adata.obs.majority_voting).loc[['Microglia','Macro_pDC']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "known-vertical",
   "metadata": {},
   "source": [
    "Actually, you may not need to explicitly convert `predictions` output by `celltypist.annotate` into an `AnnData` as above. A more useful way is to use the visualisation function `celltypist.dotplot`, which quantitatively compares the CellTypist prediction result (e.g. `predicted_labels` here) with the cell types pre-defined in the `AnnData` (here `cell_type`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "taken-action",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 427.68x676.8 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'predicted_labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "simple-klein",
   "metadata": {},
   "source": [
    "For each pre-defined cell type (each column from the dot plot), this plot shows how it can be 'decomposed' into different cell types predicted by CellTypist (rows). You can also change the value of `use_as_prediction` to `majority_voting` to compare the majority-voting result with the pre-defined cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "incorrect-transmission",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 427.68x374.4 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intellectual-mission",
   "metadata": {},
   "source": [
    "## Multi-label classification by utilising a probability threshold\n",
    "In this section, we show the procedure of transferring multiple cell type labels from built-in models to the query dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "isolated-pavilion",
   "metadata": {
    "tags": []
   },
   "source": [
    "All cell types from the CellTypist models are trained in an one-vs-rest fashion, resulting in independent probability estimates that can be compared across cell types. Probabilities are transformed from the decision scores by the sigmoid function, and are kept as is without summing up to one for each query cell. Through this, a probability threshold (default to 0.5, `p_thres = 0.5`) can be used to determine the cell type(s) assigned to a given cell."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "photographic-bachelor",
   "metadata": {},
   "source": [
    "Turn on the multi-label classification by setting the `mode = 'prob match'` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "continued-warren",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🔬 Input data has 500 cells and 18950 genes\n",
      "🔗 Matching reference genes in the model\n",
      "🧬 3278 features used for prediction\n",
      "⚖️ Scaling input data\n",
      "🖋️ Predicting labels\n",
      "✅ Prediction done!\n",
      "👀 Detected a neighborhood graph in the input object, will run over-clustering on the basis of it\n",
      "⛓️ Over-clustering input data with resolution set to 5\n",
      "🗳️ Majority voting the predictions\n",
      "✅ Majority voting done!\n"
     ]
    }
   ],
   "source": [
    "# `p_thres` defaults to 0.5.\n",
    "predictions = celltypist.annotate(adata_500, model = 'Immune_All_Low.pkl', majority_voting = True, mode = 'prob match', p_thres = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "objective-motorcycle",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "adata = predictions.to_adata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "incredible-reproduction",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "international-second",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 989.28x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['cell_type', 'majority_voting'], legend_loc = 'on data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "short-cemetery",
   "metadata": {
    "tags": []
   },
   "source": [
    "With the mode of probabilistic match, `Microglia` is predicted as `Unassigned`, and `Macro_pDC` is predicted as `Macrophages|pDC` (which follows the naming scheme of `celltype1|celltyp2`)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-sauce",
   "metadata": {
    "tags": []
   },
   "source": [
    "The probability estimates can be inserted into the adata as well by setting `insert_prob = True` in the `to_adata` function. After the insertion, multiple columns will show up in the cell metadata, with each column's name being a cell type name (no `prefix` by default) representing probabilities of this cell type distributed across the query cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "helpful-concrete",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "      <th>Plasma cells</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.999847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.999676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.999786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.994295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.998509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell496</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell497</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell498</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell499</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell500</th>\n",
       "      <td>Macro_pDC</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cell_type  Plasma cells\n",
       "cell1    Plasma cells      0.999847\n",
       "cell2    Plasma cells      0.999676\n",
       "cell3    Plasma cells      0.999786\n",
       "cell4    Plasma cells      0.994295\n",
       "cell5    Plasma cells      0.998509\n",
       "...               ...           ...\n",
       "cell496     Macro_pDC      0.000075\n",
       "cell497     Macro_pDC      0.000130\n",
       "cell498     Macro_pDC      0.000004\n",
       "cell499     Macro_pDC      0.000062\n",
       "cell500     Macro_pDC      0.000244\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = predictions.to_adata(insert_prob = True)\n",
    "adata.obs[['cell_type', 'Plasma cells']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "overall-tenant",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1483.92x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['cell_type', 'Macrophages', 'pDC'], vmin = 0, vmax = 1, legend_loc = 'on data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promising-schema",
   "metadata": {},
   "source": [
    "Examination of probability distributions of `Macrophages` and `pDC` shows the co-existence of their signatures in the doublet cluster `Macro_pDC`, as well as noticeable `Macrophages` scores in the `Microglia` cluster. Thus even CellTypist assigns the `Microglia` as `Unassigned`, the probability scores still indicate their possible transcriptomic similarity with `Macrophages`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wired-baker",
   "metadata": {},
   "source": [
    "## Multi-label classification using a custom model\n",
    "In this section, we show the procedure of generating a custom model and transferring multiple labels from the model to the query data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comprehensive-hardware",
   "metadata": {
    "tags": []
   },
   "source": [
    "Download a dataset of 2,000 immune cells as the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "searching-extraction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e954daea41634b26932cec8fca51fd1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/34.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "environmental-consumption",
   "metadata": {},
   "source": [
    "Use previously downloaded scRNA-seq dataset of 500 immune cells as a query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "unknown-surfing",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_500 = sc.read('celltypist_demo_folder/demo_500_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_500_cells.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quantitative-example",
   "metadata": {
    "tags": []
   },
   "source": [
    "Derive a custom model by training the data using the `celltypist.train` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "basic-encoding",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🍳 Preparing data before training\n",
      "✂️ 2749 non-expressed genes are filtered out\n",
      "⚖️ Scaling input data\n",
      "🏋️ Training data using logistic regression\n",
      "✅ Model training done!\n"
     ]
    }
   ],
   "source": [
    "# The `cell_type` in `adata_2000.obs` will be used as cell type labels for training.\n",
    "new_model = celltypist.train(adata_2000, labels = 'cell_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "union-breakdown",
   "metadata": {},
   "source": [
    "By default, data is trained using a traditional logistic regression classifier. This classifier is well suited to datasets of small or intermediate sizes (as an empirical estimate, <= 100k cells), and usually leads to an unbiased probability range with less parameter tuning for multi-label classification. Among the training parameters, three important ones are `solver` which (if not specified by the user) is selected based on the size of the input data by CellTypist, `C` which sets the inverse of L2 regularisation strength, and `max_iter` which controls the maximum number of iterations before reaching the minimum of the cost function. Other (hyper)parameters from [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) are also applicable in the `train` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "green-bulletin",
   "metadata": {},
   "source": [
    "When the dimensions of the input data are large, training may take longer time even with CPU parallelisation (achieved by the `n_jobs` argument). To reduce the training time as well as to add some randomness to the classifier's solution, a stochastic gradient descent (SGD) logistic regression classifier can be enabled by `use_SGD = True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "instructional-merchant",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For illustration purpose; below is not run for this small training data.\n",
    "#new_model = celltypist.train(adata_2000, labels = 'cell_type', use_SGD = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accepted-reflection",
   "metadata": {},
   "source": [
    "A logistic regression classifier with SGD learning reduces the training burden dramatically and has a comparable performance versus a traditional logistic regression classifier. A minor caveat is that more careful model parameter tuning may be needed if you want to utilise the probability values from the model for multi-label classification. Among the training parameters, two important ones are `alpha` which sets the L2 regularisation strength and `max_iter` which controls the maximum number of iterations. Other (hyper)parameters from [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) are also applicable in the `train` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-oxide",
   "metadata": {},
   "source": [
    "When the training data contains a huge number of cells (for example >500k cells) or more randomness in selecting cells for training is needed, you may consider using the mini-batch version of the SGD logistic regression classifier by specifying `use_SGD = True` and `mini_batch = True`. As a result, in each epoch (default to 10 epochs, `epochs = 10`), cells are binned into equal-sized (the size is default to 1000, `batch_size = 1000`) random batches, and are trained in a batch-by-batch manner (default to 100 batches, `batch_number = 100`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "secret-afghanistan",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# For illustration purpose; below is not run for this small training data.\n",
    "#new_model = celltypist.train(adata_2000, labels = 'cell_type', use_SGD = True, mini_batch = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-vienna",
   "metadata": {},
   "source": [
    "In general, for a single-label classification problem, all the three training approaches yield comparable prediction accuracies. For a multi-label classification problem, traditional logistic regression classifier requires less parameter tuning but needs longer training time, opposite to the two SGD-based logistic regression classifiers. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "powerful-quarterly",
   "metadata": {},
   "source": [
    "This custom model can be manipulated as with other CellTypist built-in models. First, save this model locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "maritime-henry",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model.\n",
    "new_model.write('celltypist_demo_folder/model_from_immune2000.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "industrial-credit",
   "metadata": {
    "tags": []
   },
   "source": [
    "You can load this model by `models.Model.load`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "sublime-questionnaire",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = models.Model.load('celltypist_demo_folder/model_from_immune2000.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "three-speech",
   "metadata": {},
   "source": [
    "Next, we use this model to predict the query dataset of 500 immune cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "induced-tribune",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🔬 Input data has 500 cells and 18950 genes\n",
      "🔗 Matching reference genes in the model\n",
      "🧬 16201 features used for prediction\n",
      "⚖️ Scaling input data\n",
      "🖋️ Predicting labels\n",
      "✅ Prediction done!\n",
      "👀 Can not detect a neighborhood graph, construct one before the over-clustering\n",
      "⛓️ Over-clustering input data with resolution set to 5\n",
      "🗳️ Majority voting the predictions\n",
      "✅ Majority voting done!\n"
     ]
    }
   ],
   "source": [
    "# Not run; predict the identity of each input cell with the new model.\n",
    "#predictions = celltypist.annotate(adata_500, model = new_model, majority_voting = True, mode = 'prob match', p_thres = 0.5)\n",
    "# Alternatively, just specify the model path (recommended as this ensures the model is intact every time it is loaded).\n",
    "predictions = celltypist.annotate(adata_500, model = 'celltypist_demo_folder/model_from_immune2000.pkl', majority_voting = True, mode = 'prob match', p_thres = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "thrown-rhythm",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = predictions.to_adata(insert_prob = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "comfortable-republican",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "rising-safety",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 989.28x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['cell_type', 'majority_voting'], legend_loc = 'on data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unique-transcript",
   "metadata": {},
   "source": [
    "Again, `Microglia` is predicted as `Unassigned`, and `Macro_pDC` is predicted as `Macrophages|pDC`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "intense-timer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Macro_pDC'}>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata.obs.loc[adata.obs.cell_type == 'Microglia', new_model.cell_types].plot(kind = 'box', rot = 90, figsize = [8, 4], title = 'Microglia')\n",
    "adata.obs.loc[adata.obs.cell_type == 'Macro_pDC', new_model.cell_types].plot(kind = 'box', rot = 90, figsize = [8, 4], title = 'Macro_pDC')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constant-nebraska",
   "metadata": {},
   "source": [
    "Based on this model, `Microglia`, though designated as `Unassigned` by CellTypist, holds relatively higher probability scores with `Kupffer cells`, signifying a possible tissue-resident macrophage type. `Macro_pDC`, on the other hand, holds relatively higher probability scores with both `Macrophages` and `pDC`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "double-engagement",
   "metadata": {},
   "source": [
    "## Examine expression of cell type-driving genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "violent-import",
   "metadata": {},
   "source": [
    "Each model can be examined in terms of the driving genes for each cell type. Note these genes are only dependent on the model, say, the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ecological-drinking",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Any model can be inspected.\n",
    "# Here we load the previously saved model trained from 2,000 immune cells.\n",
    "model = models.Model.load(model = 'celltypist_demo_folder/model_from_immune2000.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "recovered-colony",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DC1', 'Endothelial cells', 'Follicular B cells', 'Kupffer cells',\n",
       "       'Macrophages', 'Mast cells', 'Neutrophil-myeloid progenitor',\n",
       "       'Plasma cells', 'gamma-delta T cells', 'pDC'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cell_types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "underlying-planning",
   "metadata": {},
   "source": [
    "Extract the matrix of gene weights across cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "afraid-boutique",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 16201)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = model.classifier.coef_\n",
    "weights.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cognitive-spread",
   "metadata": {
    "tags": []
   },
   "source": [
    "Top three driving genes of `Macrophages`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "conditional-commitment",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SLC11A1', 'OLR1', 'FBP1'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_cell_weights = weights[model.cell_types == 'Macrophages']\n",
    "top_3_genes = model.features[macro_cell_weights.argpartition(-3, axis = None)[-3:]]\n",
    "top_3_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "joined-disability",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1614.38x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check expression of the three genes in the training set.\n",
    "sc.pl.violin(adata_2000, top_3_genes, groupby = 'cell_type', rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "above-merit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xuW7/stfcchV0XYfP57NwdoyxVDjwyYra4smlatW2PU3X4vWb/0tO+3NRFaanZ0yZGytvRuCTMz4ZY4yx3ASDQQCLgU/jZ8YYK2XGvakryb2pYWzuIoLR+WWvGYFS4/2Msfzi5kasqM3OzkImG2zkNGV/LtmLmemLpuyLlbfFpe6c8ckYY4zlwgh0OpwuKIoNgUCgwDNijLH1m52dBQC4JG/KMdf1vHvVa8Z44/1W8Pv98HpTz4uxjYwDn6yozc7Owq14QUSm7M8leTHsmzVlX6y8GQFPXurOGGOM5cYIdDocLtgdTg58MsbKwuzsLAgEp1SR0/ucJgc+hRD46Ec/iosXLiRee/HFF3HgwAF86MMfxpvf/GZTjsNYueCl7qyozc7OwilVph0z4HsVAKDqUYSiC2nHOiUvFgILUFXVtDmy8sSZnowxxtjaGIFOu8MJu8PJS90ZY2VhdnYWTqUCEsk5vc8lxwKfZtX49Pl8eOmllxDxz8IWmIMtMIdr9uzE/Jwfzz77rCnHYKyccOCTFTWfzw8Hkgc+z0y8iHuP/j88+uo3IITAqfHn8Nj576Tdn/F0zu/3mz5XVl6MuKcAB0AZY4yxXCwGPl1wOF1YWEj/YJoxxkqBz+dblZRzYuxpjM71pX2fcQ9qVsbn+Pg4AKDH68ElDdWJL69NwejoqCnHYKyc8FJ3VtT8Pj+cUlvSbTsarsDWukshS7F/xpc0X49Lmq9Puz+nHDtR+f1+1NbWmjtZVp4485MxxhjLiRHodLhcsDlcvNSdMVYWZmdm4aTlgU+JZBClzyeTSYFDdpuW8WkEPl3K8sxTlyJjjAOfjK3CGZ+sqM3NzcEheVJuN4Ke2XJQbF+c8cmyxXFPxhhjLDeJwGc843N+fj7DOxhjrPitDHxOB0fRXNmDpoqujO91yV7TurobWZ1OZXk4x6nImJmdRTQaNeU4jJULDnyyoiWEwPz8HBw5Fo9Ox1hmwBfgLBNe4s4YY4ytjXGd5XC54XS6Mc9L3RljZWDW50vU6wSAizMn0T9zKqv3OqjStOSbsbExyBLBLi0P57hkCUKIREYoYyyGl7qzohUKhaBqKhyS27R9Gtmjc3Nzpu2TMcYYY4zFfOxjH4PL5YLNZocsK3C43Jjn6y7GWIkTQsDv96HbvZjxeaD19QCA8fl+NFZ0pn2/iyoxMz1rylxGR0fhstlARMted8aXvo+NjaGtLXm5OMY2Is74ZEUrsUwqzVL3XNnjQVTO+GSMMcYYM9/HP/5xuFwuOF2x6zeny42FhQUIrh3DGCth8/PzUDUVLsm77PWFiA8/OXN7xvc7JROXuo+MwEGrXzdqfnKDI8aW48AnK1pGcNJuasZnbF/cXZQxxhhjzHz19fVYWFiAwxW75nI4PdA0DcFgsMAzY4yxtTM6si9d6g4AHnsV/vTgZzO+3y174fP7THkINDIyAoe0OvK5NOOTMbaIA5+saCXqQ5F5gU+ZbJAlG2d8lhBVVfHkk09iYmICd9xxB4aGhgo9JcYYY4yl4ff7FzM+3bH/8rUXyzcichLRi0R0lIhOEtGnkowhIvoKEZ0jomNEdGkh5sqKXyLwKVWt6f0uqQqqGkUgEFjXPMLhMGZ9Pjx/9DgC4ciybTIRXDaFMz4ZW4FrfLKiZZwUbJLL1P06JNe6Tzgsf44cOYK//du/xeWXX47f/d3fhdfrzfymdbjtttvQ0dEBQuwpKiVZRsIYY4yx1Obm5uB0xxpKGgFQv9+PxsbGQk6LbTxhADcLIeaJyAbgaSJ6VAjx/JIxbwawNf51JYCvxf/L2DLGMvWVGZ/ZcsmxgOn09DQ8nrWXcjOyOd929eVwO+yrtjskCSMjI2veP2PliDM+WdEygpN2MjfwaePAZ0mJRGJPMoeHh3HttdeisrIywzvW57/9t/+Gt771rYsBT458MsYYYzlZlvHp4saSrDBEjJFqbIt/rVxn/E4A346PfR5ANRG15HOerDRMTU0BANxy9Zre714S+FwPI5vTqOe5kkuWMDI8vK5jMFZuOPDJilYi8Jkh47Nv5gQePPVvWe/XTk4OfJYQo1vhyq6FVnE4HFAUToZnjDHG1mpubj6xxN0Vz/z0+/2FnBLboIhIJqJXAIwD+KUQ4oUVQ9oADCz5eTD+GmPLTE9Pg0Crmhtlyy1VAwAmJyfXNY+MgU9FxsTkJHRdX9dxGCsnHPhkRcsogm8jZ9pxHVXbceOm92a9XwWc8VlKjIBnvrvBJgKu4IxPxhhjLFuqqiIQWIDLHVuh4XJzxicrHCGEJoTYD6AdwBVEtGfFkGQXeqsuOonoViI6RESHJiYmLJgpK3aTk5Nw26og09oSJDxyDYDFzNG1Gh0dBRHBIScP5TgVGaqqrvs4jJUTDnyyorVY4zN94FORbKh2NmS9Xxs5EVjgwGepkKTCfEzlO9OUMcYYKwdGZqeR6enyxAKgPp+vYHNiTAgxC+AJAG9asWkQQMeSn9sBrFonLIT4uhDioBDiYEND9vcdrHxMTk7CLdWs+f1OqRIyKaZkfLpsCqQU9yhGJig3OGJsEQc+WdEKBoOQSIYMm6n7tZMTwWDI1H0y6xQ+8FmQwzOWtWy61jLGWL4kAp+eWODTZndAVhRe6s7yjogaiKg6/r0LwC0AzqwY9hCA98e7u18FwCeE4M4wbJXxsQlUUN2a309EqLDVrjvwOTIyAqeU+gbFpcTunTjwydgiLmTHilYwGIRNcpiecaeQEz5e6l4yZDn21DLfS90NnPHJSkA2XWsZYywvjMxOI+OTiOB2V3LGJyuEFgDfIiIZsYSf+4QQPyGiDwKAEOJ2AI8AeAuAcwACAP6kUJNlxW18fBxd8qZ17cNDdRgdHVvXPkZHRuBMscwd4IxPxpLhwCcrWrHAZ/pl7gCgCx0QApKUvMDzSjbJiVAot4zPcDictgOfrut45plncN111yVeq66uhstlbkf6jcgIfOY7AGlkmhJxYjwrbiL2VCBT11rGGMsLI8Dpji9xB2LZnxz4ZPkmhDgG4ECS129f8r0A8OF8zouVnkAggLl5Pyqr6te1n0q5HmMjr635/aqqYnJqCpu87pRjFEmCQ1EwNra+ACtj5YQDn6xoBYPBjI2NAOCFgZ/AF5rEm7b9aVb7tZEj0TgpWx/58Idx+szKlTGLdF3H0NAQOjoWSwS1trTg+/fdxxmD61SowCcvdWelJJ7N8jKALQC+mqRrLWOM5cXKGp8A4OSMT8ZYCTOyJyvldQY+lQacnXoGqqpCUXIPxUxMTEAIkbKju8EpSxgZ4YoNjBk48MmKVjAYhEKOjOOu7nxHTvu1kQOqGs36hDM3N4czr76KFo8T9U47AEDTNISjKtzOxfnta6hOfD8TjmJwZATDw8Noa2vLaX5sOSPzsmBd3TnyyUqAEEIDsD9ey+xHRLRHCHHC2E5EtwK4FQA6OzsLM8kc/fjHP4aiKLjuuuvwyCOP4ODBg9iyZUuhp8UYy2B2dhYA4K7wJl5zeyow6+PsI8ZYaTICn16lMeWYiYVBLERm0V2zJ+WYSrkBuq5jcnISzc3Na55H5sAnYZQDn4wl8BpOVrRCoRAU2E3frxJfPp/tcvcTJ05ACIGOChfaK91or3RD0VSMTYwnfl751RVffnDixIkMe2eZGBmf+RIMBnHTTTctCXzyxyQrHam61pZiN9qrrroKV1xxBZ588kncdttt+PKXv1zoKTHGsuDz+aDY7LDZFx8OuzyViYAoY4yVGiN7sjJN4HMhMovZ0ETa/RiB0+Hh4TXNI9vAp0uRMTo2VrAeCYyt18LCAjRNM21/fEfPilYwEISSxVL3XNkoFkzNNvB5/PhxEBGqHIvd5dvqanDdru0p31NpU2CTZRw/fnx9k2V5X+rucrnw+OOPL6nxyRmfrLhl2bW25DQ1NaGhoQGqqgKI1ddijBU/n88HT0XlstfcnkrMz81B1/UCzYoxxtZuaGgINskBt1SVckx3zR7sb7kp7X6qlCYAaw98GnU7nRkSQ5yyjHA4nCg9wlgpeOaZZ/Dbv/PbeOCBB3DbbbfhySefNG3fvNSdFa1gMAQbVWYemCMjmJptnc/jx4/Da7dBkbJ/TkBEqLLLOHr06JrmyBYVfKm7xIFPVvSSdq0t8JxMw2UnGCsts7Ozy+p7ArGMT13XMTc3h6qq1IEDxhgrRkNDQ/Aqjeu+FqmQ6yCRjMHBwTW9f2xsDE6bAlkiBMIRLIRCaKjyrhpnZISOjY3xZy4rGadPn8bE+ASOHTuGT33qU6bumzM+WdGKNTfKXONzpe++8n/Tbjf2mU3Gp6qqOHXyJKrtuT8jqHbYceHCBczPz2cezFJaS+FvMxgBVzmHgDdjhSCEOCaEOCCEuEQIsUcI8elCz8lMhXr4wRhbm9nZWbg8izfiH//z3090eOcGR4yxUjTQP4gquWXd+5FIRpWtcc2Bz9HRUTjj10W+hQCGpmcBAP/x88ehL7lOMgKfxtJ4xkqBlUkOfEfPilY4HM6qudFKl7W9Me12JYfA5/nz5xGORFC9ZJl7tmocNgghcPLkyZzfyxYVKtuLs8wYKw5cdoKx0rIy4/MzX/7PROBzZmamUNNijLE10TQNwyPDqFJyb0aUjFdqxuDA0JreOzIyAqccux5qqa3G/p5Yw8r//sabIC25TnJy4JOxZTjwyYpWOBzKKfA5FYjVStnRcEXacTYp+8Cn0Zyoxpl7k6Uqhw0EbnBklsJ1deePScYKiTM+GSstPp9vWUd3m90BF2d8MsZK1Pj4OFQ1imqTAp9VSjMGBwdyvq4RQmB8fDxjYyMAsEsEWZISNUEZKwVWJjvwHT0rSkIIhMKhRJAyE1WL4N6jn81qbC4ZnydOnIDLpsAp5/6rYpMkVDrsHPgsUZxlxlhxyHeDM8bY2qmqioWFhUSg02AEQrnRBmOs1BjL0quUzEvdR/znMeI/n3ZMtdKCUDiEqampnOYxOzuLSCSSyOZMh4jgsskc+CxRv//7v4+hoSGcO3cOn/jEJ/C5z31uQzQHtDLZwbLAJxHdSUTjRJQ06kNENxKRj4heiX990qq5sNITiUQghMg641OR7fjI1bdlNTaXGp8njh+H1yavuuHWdB1zWTRHqrLJOHXq5Ib4oLJaoZa6S9zciLGCMgKfnPHJWPHr7e3FqVOnEkvbDbzUnTFWqvr7+wEANbbWjGN9oUn4QpNpx1TH92PsN1tGEHNlxmcgHEl6jeQkwsjISE7HYMXh3nvvRVtbG55//nk8/fTTePjhhzfE+dO45reClRmfdwN4U4YxTwkh9se/yqoZA1ufcDgMAGtqbpSJEUw1jpHKzMwMRkZHUe1Yvcx90j+HQ+cuZDxWtcOGhYVAzic2tlqhlrpL3NyIsYKy8iKIMWYuRVGwa9euVV3dbXYHbDY7Z3wyxkrOwMAAbJITbqk649gdjVdiR+OVaccYS+ZzbXBk1Ot0rbgueuLEaUzNrW6m61JkjHLgsyQZ175Lk6c2QiKVlffdlu1ZCPEkgGmr9s/Km5GNKVPutTUzUeL7DGbI2Dxz5gwAJG1s1FRdhZv27sx4rKr4e0+fPp3rNFmBcXMjxoqDoigA+HeRsVJg1PB0eSpx+IUnEQwG8IkPvSfx2uzsbAFnxxhjuRscHES1rTnr65CFSPpaxhVyPWRS1h74XJHx+ZbL9sHjcODsyPJl7S5Fhs/vz3jPy4qPECLxZdgIgU8rr/kLncp0NREdJaJHiWh3gefCiogR+CxkxuepU6dAALx2Zc3HqrApUGSJA58myHfQw3jiJEmcbcZYIfFSd8ZKhxH4dFdU4vyrJ6HIMv7p378fe81Twc2NGGMlp//iAKqk7Bsb3fbcR6CL1EEqiSRU2ZowMDCQ0zxGR0dhk2UoScpwabqOYDiy7DWjFijX+Sw9d999N+64444Nd+1r5TV/IQOfhwF0CSH2Afg3AA+mGkhEtxLRISI6NDExka/5sQIygpJKFhmfUS19AHMlhWzLjpHK6dOnUeGwQVlHyjURwWtTcOrUqTXvg8Xk+4N/sblRXg/LGFvBePrLGCt+iYxPdwV+7/1/Dps99rA5Go3A6a6Az8dL3RljpUNVVYyNj6ZsbDTgexUTC8szNz9+w39CovT3j1VSCwb6c8v4HBkZgUtZ3XsCACpcTlzS3bHsNXc88Ml1PkvPn/zJn+ADH/gAotEootEogI2RAFCqNT7TEkL4hRDz8e8fAWAjovoUY78uhDgohDjY0NCQ13mywlgMfKbP+NR1DZ/7zR/ltG8iCYpkzxj4fPXMGXiz6JqXideu4Py5c1BVdd37YvnDNT4ZKw5c45Ox0mHU8FzZ1f0zf/3foNgcXOOTMVZSxsbGoGkaqpTkGZ9TgWH4MzQzSqZKacbwyHBOwazh4SE4c2i6aiyJN5bIs9Jz4cIFjI+PF3oaeVOWS92JqJnifyIiuiI+l6lCzYcVFyMoKdPq+ppLSZKMv7v5vpz3nynwOTk5iZnZWXiT1PcMR6M4Mzic9bG8dhuiqoqLFy/mPE+2qFBL3bmuIGOFZQQ++XeRseLn9/uh2Oyw2Zav2PnUv34LlVU18Pl5qTvLHyLqIKLHieg0EZ0kor9IMuZGIvIR0Svxr08WYq6sOA0Px+75qpSmpNv3t9yEzXX7c95vldKESCSMqanswh9CCIyMjK6q75mOQ5YgS9zZvZRt3boVbW1thZ5G3li5ysuywCcR3QvgOQDbiWiQiP6MiD5IRB+MD3k3gBNEdBTAVwC8V2yE/F2WlVyWuq+FQra0gc+zZ88CiAUtV9J1gVA0++xNo0aosU+2NtzVnbGNiTM+GSsdfr8fbk9F0m0udwXm5+Y2xHI9VjRUAH8lhNgJ4CoAHyaiXUnGPSWE2B//+nR+p8iKmRH49CqNGcdOB0fx/WP/lNV+jf1lG5T0+XwIhUJw27K/JiIiuBQl8WdgpW0jJAAY991WXCdYFlIVQvxBhu23AbjNquOz0haJxIoz5xr4vDh7Cl3Vya5nlpMpfcbnuXPnAACVttW/Ii6HHft7OrOek8emQJYI58+fz/o9rPAWmxtx4JOxQuLmRoyVDr/fD6fLk3Sby10BXdexsLCAiorkwVHGzCSEGAEwEv9+johOA2gDwMX3WVZGR0chkQyPXJtxbLWjAdf3/H5W+61UYuX7RkZGsHfv3ozjh4aGAKzu6J6JU6acu8ez4rH0PnQjBD6txHf0rCitNfD5y7PfyurmWCF7olBwMn19fXDbbLDJqX9FHnrxMCayKNIvEcFjs3Hgc40K9SFvHJdPMowVFgc8GSsdfr8fjhSBTyMgOjc3l88pMQYAIKJuAAcAvJBk89VEdJSIHiWi3fmdGStm4+PjqFBqMzYrAmIl2FoqN2W13wq5PrH/bBiBT3eKwOfpgWEMTk2vet0dz/jka6nStPQ+dCMl41hx/82tUllRMgKfmWp8rvSByz+X1TgZSuIYyfSePw+3kv4X7s2X7YMtyyWYFYqEXg58loSRkREcP36cMz4ZKxL88IGx0jE3Pw+nqzrpNqd7MfDZ0pK8QzJjViCiCgAPAPhLIcTKrIXDALqEEPNE9BYADwLYmmQftwK4FQA6O7Nf+cVK2/j4ODxS5mzPXNklJ5yyJ+vAp7Fc3Z2iBmKdtwJO2+r7ZrciIzA3B5/Ph+rq6jXPlxXGRs34tCJQz3f0rCgtBj6tic1LsKUMfOq6jov9/ahIssx9qWyDngBQYVMwOTWFhYWFnObJFuXrSaWmaYhEIhvq5MIYY4yZYX5uftlS9wvnzuBr/xLrFeNwuWNj5ucLMje2MRGRDbGg53eFED9cuV0I4RdCzMe/fwSAjYjqk4z7uhDioBDiYENDg+XzZsVhYnwSHqkm7ZjvH/scVD31SsJU3HJN1s2NBgcH4bLZIMe7uk/453Ckd7FxbmOVF163a/Ux4jVBebl7adqoGZ9W4L89VpRUNdY8SLIo8CnDhmgk+QlqbGwM0Wg0Y+AzF574vvr7+03b50aTr0Bke3s73va2t3HGJ2NFwnjowQ8jGCt+8/PzcLgWb767t+zAB/7n/wYAOJ2exBjG8oFiJ447AJwWQnwxxZjm+DgQ0RWI3R9nF41iZW96ehouuTrtmEuab4Ai5bZKEQBcVJV14HNgYAAuefE6qMLhQHN1Vcb3eeIZohz4LE1L70M30j2pFdf8G+dvj5UUo/6mjNQnEU1X8cyFH61p/xIpiKrJO7MbJwZ3FoHPCd8cZuYzZ3F6+GlbydpIJxnGihkHPhkrbrquIxgMwOF0L3vdZncAABzOWECUV7+wPLoGwB8BuJmIXol/vYWIPkhEH4yPeTeAE0R0FMBXALxXcEFEhtj9aCC4ALfkTTtuZ+NVa9q/S/JidsaX1diB/n54ltT3dDnsaKmtzvg+t00GIRY4ZaVnoy515xqfbMMwAp8SpV5OLiAQ1oJr2r8EGdFo8vcawUlPhq55qqbjxy8exhVbN6GmInkhf4Obn7aVnI10cmGsFPB9KGPFLRQKQQgBh2P1cktgMfDJGZ8sX4QQTwNIe0EnhLgNwG35mRErJUYjNqdUmdX4Lz79AXz02m/i4uwpDPnO4nVd70w73ilVYtyXOfDp9/vhn5tDa01281hKIoLbbuPAZ4laGviUcyizV+p0XTd9nxz4ZEVJ0zQAsQBlKopkw82b/3BN+5dJhhpNnvE5PDwMWZLgSNPRHQAUWcIH3nBDdseTCC6bgpGRkZznygqDl7ozVhyMhxAc+LTe/Pw8KioqCj0NVqKMTE4jwLmSPf56IBDI25wYY2ytjIc0dsmdYWTMn1z2jwCAGmcTpCwW1tolNwKBAIQQaRMujFJpxgrCXLllCRcvXFjTe1lhbdQan5zxyTYMVVVBJFmWdUeQocaDqyuNjIzAZZMzHlvTdcg5fAA5ZQkjI8M5zZMVjvH/nzM/GSsO/Ltontdeew3/8H/+AdElDwAXFhZw5sxpXHbZwWVjd+zcgf/7fz+d7ymyEmQENO0OJ37wn7fjje94LyqrqhPbFcUGWVE48MkYKwmJzzQp+cMcw5DvLH7dew/+6MD/AQCcHH8alfZadGBH2vfZJRdUTUUkEoHD4Ug57kI8aOlZY/8Jj03GwOAgdF3fUMGzcsA1Ps2z5r89Ivq6mRNhbCld17N6UrZWEqSUKdRjo6NwZPhli2oavv34MzkdMxb4HM3pPaxwOMjCWHHhjE/z/OpXv8Lg4DC8/s2JrxbtEty09T3LXhPTlXj88V9jfHy80FNmJSAYjJUQsjuc2LXvIFye1WWA7HYHQqFQvqfGGGM5C4fDAACF7GnHtXq34Hd2/yUAYMR/HtXOJnTV7M64f2O/xnFSuXjxImSJ4M5Qhi2VCpuCSCSC0VG+Dy01G7XGpxXX/GkfGxBRbapNAN5i+mwYi4ul/GcOfPpDU6h01Ob+QUCUOvA5Pg5XhhOLTZbxJ6+/LqdDOmUZA5OTGZczsOJg/D/aSE/XGGMbw+GXD6PZvgVvqPsfacdNRPrw/bGP48iRI3jjG9+Yp9mxUmUEPm12J7ZuTX7Tb7M7E+MYY6yYRSIRAICcIfBJRPDYYx3WfeEpRNQQIlrmBzwK2ZYdJ5ULFy7AY7Ot+f6xIp4p2tfXh9bW1jXtgxUGBz7Nk+mOfgLAIQAvL/k6FP9qNH02jMVl+4/9x6f/DePzF3PeP4GAJIfQdR2zs7NwZqjvuRYOWYKqqolC2ay48VJ3xlg5mp+fx2uvvYY2x/LA1HRwFE/23b/stXpbF5xyBV5++eV8TpGVKCOT0+jinoyNMz4ZYyXC6DlBOaxC3NFwBVz2Crwy/OuMY439Zmrk0tfbC4+y9ntTI/B5get8lpyl96F8T7o+mX6DegHcKIToWfK1SQjRA2AsD/NjGxilb8IIAPijA/+ApsruNe1fJIl8+nw+6LqesbHRWhj7nJqaMn3fjDFW7viCzxyvvPIKdKGvCnw6ZTfq3W3Q9MW6n0QSWu27cPjQ4XxPk5WgcDiMSCSCb//HF1KOsdnsHPhkjJUEIxEn16uPbfUH8fot78s4zgh8pkv4CQaDGB0bSwQv18ImS3DZFPT19a15H6zwNtJ1cCFqfH4JQE2KbZ83dyqMLZcsMGmmZIHVmZkZAIA9Q+DzteFRzAVjF+4/ePYlvPDqeUzNzad9jxH4NI7BihtnfDJWXLjGpzmOHDkCWbKh2bFt2etuuxdRPYKHTn912ettjt0YHR/F8DA352PphcNh2O12/OGf/s+UY2SbLeOyTsYYKwayHCt9ls096Ree+lOoejSn/evQlx0nGSNLcz2BTyDW2f38+fPr2gfLv42a8Zn3Gp9CiK+m2Xy7yXNhLIGILL3JFdBB0uoPj9nZWQCAPUNdR13XE/O7ee9ORFQNFU5n2vfY4oFPn8+3hhmzfOPAJ2OsHL186DBabNuSNmvY13Ij9rXcuOy19nhm6OHDh7k2GEvLCGhW1zWkHKMoHPhkjJUGRYmFSnShZRz736/4AhTJltP+dRFbYZEu8GkEKyvt6wt8VtoVXLhwAZqmpT0eKy4bNfBphZzW81LMzUT0TQCDFs2JMciyDIH09U4AQNNV6Hrmk9FKQuhJP/SN+pu2DBmfO9pb4XW7AAC1lRVorqmCI8OTOFs8mMo1PksDBz4ZKw78O2gev9+P873nVi1zT6fW1gG34sWRI0csnBkrB9FoLNtJVlLf/MuKLTGOMcaKmd0ee0CoiswPayoci4tk7z/+L5iPzGZ8j7FfhyN1XeS+vj7IkpSxo/vJ/iFoaWqFVtgURKNRDA0NZZwXKx4bNfBZiKXuxoGvJKIvA7gI4CEATwHYYfpsGItTFAW60DJmff669x48N/BwzvvXkfxpVyLwaUEnb1s8w5QDn4wxlj1e4m6eV155BUIItDlTBz5PjD2N0bnFOmBEhFbbLrx86DD/v2BpqWrm7CVZVhCNqim3M8ZYsXDGV/NFRW51iQ+2vRFupTLjOFWEAaQPfJ4/fw4VNiVjIGguGEK6U3SlPfZAiut8slKQ967uRPSPRHQWwGcBHAdwAMCEEOJbQgguVMgsk1hagPTZnA2eDixk8URtJU2osNlXZyQEAoHY8S14yiBTrKro/Hz6WqCsOHDGJ2PFhX8X1+/IkSNQJDua7FuWvX7bcx/BbHAcACCTAqLll4dtzj2YnJrgOp8sLSPwKUmpA5+SJEHTOPDJGCt+FRUVAICIHkw7TtWWZ4T21O5N+zloiOhBuJyutA+Lzp07hwpb5n1dtX0zlDQrFo199Pb2ZtwXK0788Hl9MqW13YpY9/avAfiOEGIKsLjjDGMAbLZYUFIT6ZdD7W58Ha7t/t2c968LNXGMpYzAp5yk/ud6EREUWeZupoyxrBHRJws9h2LBF3zrd/jlI2i2bYdMy89/H7j886h2NQIAdjZehaaKrmXb2xy7AICXu7O0NC32sFpKcxNPkpQYx9h6EVFFoefAypcR+AzrqZNW/OFpfOW5DyV+fnXipaz3HxbzcLs9KbfPzs5idtaHynU2NgIARZLgsdu4wVGJ4Wtf82QKfDYD+EcA7wBwjoj+E4CLiNb/28dYGkbKf6bAp012wG3LvJRgJRUROByrGzuEQiFIRJAsyixSJAnBYPqnhqw4cMYnKxIfKPQECo1/B83h9/vR23cebY6dq7Y5FXfa99YobVznk2Wkx+vLXTz/Gr71tX/Gc0/+YtUYiaS0degYy9GpQk+AlS+PxwNZlhHSU5cp8zpq8dFrv5n4+aXBR7Pef1CbQ3V1dcrtRnbmehsbGSoUCefPnzNlX4xZyYpr/0xd3TUAjwJ4lIicAN4GwA1giIgeE0L8oekzYgyLgc9sikmvhYYInM66Va+HQiHIGep7RjUNL7x6Htfu2pbzcSUCZ3wyxpYhIn+qTQBc+ZwLK1/Hjh2DEAKt8ezNXBARWmw78cqRoxbMjJULIzNF1zScOfEy3vPHH141hoggOPDJckBEH021CQBnfDLLEBGqvNUIRlJdpq32vgPZL9QJCj86aqtTbjfqcVaYkPFp7Kd3cAjhcDhtXVFWPJZmfAohNkwyQN5rfK44eEgI8QMhxO8C2Arg56bPhrG4tRaTzlZUhBPHWCoSiWRc5i6ThMZq75qOKxNxN9MSwRmfLI9mAWwVQnhXfFUCGCnw3FiZOHr0KGRS0OTYknlwEq2OnRgbH8XY2JjJM2PlZsuOPfibz34NTleSTGKitA04GEviswBqAFSu+KpADveyjK1FXV0tFjRrWpsE9VnU1a1OxDH09vbCrshwpKndCQC9o+NZHa/SrkAIgf7+/pzmyQpnZeCz3Fl5372mk4UQwr/W9zKWDZcrluQU1a0JfKoilDjGstdVNeMyd0kibGttxsjMLB47ltsKGwI48MkYW+nbALpSbLsnnxNh5euVV46i0b4ZCi2WeTk2+iSe6P1+Vu9vjS+RP3qUsz5ZerrQVzXIShAC/DyR5egwgAeFEJ9a+QUg9RpkxkzQ0NiABTGdcvuI/zx+cOKLOe9XCB3z6gzq6+tTjunt7YVHkdMGg4QQODs8hnuefC7jMY3M0QsXLuQ8X1Z4GyHwaaX1BC8/ZdosGFvB44kVeo6I7OphzgTH8Mir38h6/2E9CLd7dSaCqqrI9nq8scqLq7ZtzvqYS4/BGGMGIcTfCSFeTLHt4/meDys/4XAYZ197DS32Hcte31Z3GS5te0NW+6izdcEuO3H8+HErpsjKgHFzPnSxF9+/6ytJxwjwSgqWsz8BkCpF7WC6NxJRBxE9TkSniegkEf1FkjFERF8honNEdIyILjVj0qw8NDY2YkGbSr29ogs39rwn5/0GdD90oaKxsTHpdiEE+vr6UKGk7+hORHjjpXvxrisz/7N12xQQkSmBz+985zv4X//rf+Guu+7CmTNn1r0/lt7s7CwiEWtKAG4UaQOf8Q//ZF/HATTlaY5sA0oEPvVAyjHfPvx/cHTkCQQifnjsVdhef3lW+9aFjogWTHTqW0rTtKwDn7IkwePMrT4KYbH4P2OMpUNE24ko+yc6jKVw5swZqJqKFsf2Za87bR54HbVZ7UMiCU22bTh+7IQVU2RlQIrXSG/r7MEH/uLvE6+/dmoxS1joOiQp/Y08Y0sJIV4FACI6SETVK7Zlqr2hAvgrIcROAFcB+DARrSx0/GbEyrhtBXArgK+ZMW9WHpqamhBU5xFJsQpRlhTUe9py3u+cOgEAaG5uTrp9dnYW8/Pz8GRZ39OdRc1OmQgem4KLFy9mP9EU7HY7nnrqKdxxxx248847170/lt7CwgL3CVmnTBmfTQDeD+DtSb5SP/pgbJ0qK2Od2sP6Qsox77/0U5gKjOD+4/8Cu+zE5rr9We3bCKYax1jK6qLBBE5TZ4wtR0SXENEviOgEEX2GiJqI6AEAj4E71jITGFmazfbcm/It1Wzfht7e8wgEUj+UZBuXLMcCmsYD3jv/7bMIhYJ4+P67E2N0oScCpIxlg4g+AOAkgH8DcIaI3pHte4UQI0KIw/Hv5wCcBrAySvVOAN8WMc8DqCaiFnNmz0pda2srAGBOza6OZrb8Wmx/LS3J/6kNDAwAyNzY6NiFAVwYn8Qrff04M5i5LLxblnDx4oXcJpuE3W6HzWZDo7sWCwup79fZ+hhxg7a2Nni9a+sxwmIyXXn8BECFEOLiiq8LAJ6wfHZswzJ+sUN6+tI9r+t8B/a2XJ/Tvo19FubDQ3Dgs8TwkjyWB99ArJbn7wKYQKyeWS+ALUKIfy3kxFh5OHnyJKrtzXDJyc97uq7hzETSagvLNNu3QRc6Tp8+bfYUWRlQlNgNuqbFSvpcdf0b4J+ZQnvXYlkgXdMS4xjL0l8C2C2EuBrA6wD8zVp2QkTdAA4AeGHFpjYAA0t+HsTq4CiI6FYiOkREhyYmJtYyBVaCjMCnTzO3sZ9fHVu2/5WMwKfbljxDXtcFguEIOupr0VhViW2tzehpSl0v1OCxyRgcHFr3CsRwOAwAqLR7EOZMRFYC0gY+hRB/JoR4OsW2P7RmSozFmhspii1j4NNp8+Ds5Ms4N3UEAHBo6Bd48GTyulIGY59VVVXmTJaVLQ5SszxxCCHuFkK8KoT4MgAdwCeEEFldSWZTw4xtXEIInDxxCo1K6m7uES2EU+PPZtyX0RH+1ClORGarJTI+NQ0AsGvf5Whsacd7/vgjiTG6pnLgk+UqIoSYAAAhRC+A3OpMASCiCgAPAPjLeJPeZZuTvGXVBaAQ4utCiINCiIMNDQ25ToGVqLa2WAx8Npo5mzIXs9FR1NbUwel0Jt0+ODgIIoIrRY3P4ZkZPPfaedRUeOB2OOB22OGw2TIe16UoiEajmJycXNf8jWXXFXYPgkEOfFphaGgI//RP/1ToaZSNNV95EFGFEGLezMkwZiAiVHmrEAyvvDZZ7Z07PwK7EuvQfrDtt3Cw7bfSjg/qsX1WV1eve56GCf8cais8kDMu3yLOICwh/f393PmQ5YOTiA5g8eZrHsAlFP+wMJbppWHUMDtMRJUAXiaiXwohODrFMDExgemZKeyu3rpqWyA6h6gWRpWzHr+z+y8z7sspVaDG3sKBT5aULX7TbWR8JqOqKtyuzDfnjC3RTkRfSfWzEOJ/pnszEdkQC3p+VwjxwyRDBgF0LN0/gOF1zJeVEa/XC2+lFz511NT9+rQRdHZ1pNw+PDwMt02BlOK+sb2uFu112dXoXsrIIB0eHk7ZWCkboVAIEklwKQ6MhWfXvB+WWltbGz7+8Y/j85//fKGnkjdWJh2tp8gOX/UyS9XW1iCg+TKOc9o8kCj7f8pBLXXgU5KkNf3Cnewfgj+QuQO9ALi2VYkgInR1daG7u7vQU2HlbwTAFwF8If41uuTnf8n05ixrmLENyui22mTfvGrb+akjODryeE77q1c24fQp7uDKVrPZbIhGo/jxfXclXjvywlPLxmhqFHa7Pd9TY6XtYwBeXvK18ueU4g8Q7wBwWgjxxRTDHgLw/nh396sA+IQQ5qb3sZLW0dmJWdXkjE9tBB0dqQOfIyMjcErmJ8u44xmko6PrC+SGw2HYZRvsso2b7lhoo8YNrEgUS5vxSUQfTbUJwOqW2IyZqK6+Dr0DmQtJ+0NTkEhChaMmq/0G9FkAQG3t6qdksiyvXtuShRv37Mh67Eb9AGOMJSeEuCnVtvhNWNbS1DBjG9Rrr70GgoQ6W9eqbXubc6uRDQCNtk04O/UMZmZmUFOT3XmXbQwOhwOyLGPT1ljTbF3XcerYIRy48rrEGJUDnyxHQohvrePt1wD4IwDHieiV+Gt/C6Azvu/bATwC4C0AzgEIAPiTdRyPlaGurk5cfO25jOMePv01XN/ze6hypq+1GdTmEFT96OzsTDlmbHQUzhTL3NfDGS9JMj6+vmZNoVAIdiUW+IxEwmZMjTFLZVrq/lkA/4zYMrqVOHrDLFVXV4cT+mtpxxwbfRJnJw+hp/YSXNp6S1b7XdBm4HFXwOFYXSJIlmXkkvB5enAY3Q31cDmyu4gXQEnWtjp58iSICBUVFaiuruaucozlz32I36Blkq6GGRHdCuBWAGkvtFn5OXv2LGrsLbBJOZfFS6rB3gMAOHfuHC6//PKc33/06FG88MILGBoawjXXXJN0jM1mwzXXXMMBshLjcDggSRJ6Nm/H+ddO4tEffRcf+fhnl41Ro5Gk11+MpUJE9QA+DGAGwJ2I3ZteB+A8YmVezqV6b7xXRdrUIRFb6vVh0ybMyk5nZyfmo48grC/AIXlSjtvZeBU8tsz3SLPqUGK/yWiahpnZWfRUutY24TRkiWBX5HUHPsPhMGySDTZJQTgSMWl2LB0ul7c+mSIwhwE8KIRYtYyAiD5gzZQYi6mvr8dCdBa60CBR8ideDZ52tFT0oKEi9VKBlRa0aTQ0Ji9KrigK9BxyPjVNh8hhfKkGPv1+Py5evIjbbrsNTU2NeOCBZCWSGGMWyOoqJ1MNMyHE1wF8HQAOHjzIXbs2kLOvnUNdmsZGKz3y6jfwW1v+KxQ5edDRyBw9e/ZszoFPv9+Pj3zkIwiHw4hEInjssccAABcvXkRVVdWyEjSf+MQn8La3vS2n/bPCcrliN+nRaASbt+1eFfQEYoHPVM08GEvhHgCHAGwF8CKAuwB8GbHg5zcB3FiwmbENwSh7NRMdQrNjW8pxW+oOZLW/6ejQsv2u5Pf7oes6HLI1eWYOWcbMzMy69rF0qbumaVBVblxnBQ52mifTv84/ATCVYttBk+fC2DINDQ0Q0BHQZlGh1CUd01K5Kef9LujT6GxMvgRBURToOaR87ulqhy8QQP/EFDobks9xKV2IkjwpXH311YmmBWNj63tCyBjLScYPpCxrmLENaGFhAeMTY9hUdXPW72n0dKZ82AgALrkSFbYa9PX15TyfEydOQAiBAy31qHMuCay2LZ6TBYCnhybxyiuvcOCzxBiZnNFI6npv0UiYA58sV01CiL+Nn+suCiH+Of76GSLiTE1mua6u2AO/6QyBT8PDp7+Gt+/885TbZ6JDsNvsaG5uTr49HpS05xD4HJv14aVzfXjbwf0Zx9og1h34jEQisEkybJKS+LkU73GL3dLAJ5fLW59M/zovAqhc+SIRNQLI3HWGsXVoamoCAMxpUykDn2sxr02huXlX0m02mw26nlsyVFTVEMwyxV/HYtfTUsMftoxZg4geRvIAJwHI5sMvaQ0zIcQj5syQlaoLFy4AAOps2a+KONj+xoxjqqV29PXmHvg8duwYJCK0eVyQ0zRtaHQ7cPTo0Zz3zworkfGZpt5bJByC2+3O15RYedCA2JJ0IppcsU0vwHzYBtPa2gqbYsNMfIl6MjPBMQgI1Lqa0ViRvqTQjDqEzs4uyHLyh4xzc3MAAFsO915N1VV444G9WY21yRJ8s7NZ7zuZcDgMGymwybbEz/zZbr6lgU/O/lyfTIHPrwD4GYCVy+beAOBaACkfZRDRnQDeBmBcCLEnyXZCbJnCWxArJP3HRldaxoDFwOe8Ngkg89O1bGgiigV1Fo2NjUm32+12aDl2da/3VqLeu+r5QFK6ECVbs4wDn4xZxujc7kJsKZ+OWO2yILLr6p6xhhnbmPr7+wEA1UprVuMvzp7CXGgKe5qvSzuuxtaKcxefghAipwvxE8ePo9JuSxv0BIBqhx2vjoxwA6USY9z0RlJ0+NV1HVFe6s5yt4mIHkLsPGd8j/jPPYWbFtsoZFlGZ2cXpocHUo65OHMSutBQ29aMKzvemnZ/M9oArtyUeln8/Pw8AEDJ8d7LtxCAIsmorkgfgFQkCQsLCznte6VIJAJFUnCq/zXouo4I1/m0BN9/myfT3+S1KWqFfRdAplagdwN4U5rtb0bsBm8rYg0XvpZhf2yDSWR8qhOm7XNOjT0oTrW0wOFwQAgBkWPwM1uaXrqBz422fMG4meenaywPngXwVgDfQazEzAcA/CdiDw+fLeC8WIkbGBgAkQSvkvxh30pOxQO3vSrjuGqlBcFQIKelcqqq4vTp06i2L55LVE3Dc2dW9yWpdsQySE6ePJn1/lnhGRmf4XAw6fZI/HXOCmI5eieALyD2IND43vj5XYWbFttIejZ1Y0YbTLl9f+vNuLTtDRn3E9GD8Ecn0dOTOmYfCAQAAEqO9yC9oxN48IWXEY5G045TiBAMJv+czlY0EoFNVjAfWuDAp4X4PtQ8mQKf6f6m075XCPEkgOk0Q94J4Nsi5nkA1UTUkmE+bAPxeDyorPDCr5pXU9KvxYKoqQKfRlAy16zPbOlC58AnY2ylzwOoAdAjhLhUCHEAwGYAVcgi45OxVIaGhuC1NUCm7D6/myq6sKn2kozjqpTmxP6z1dfXh3AkkghqAoBEEqo9q4NgVXYbiIgDnyXG44l1O46kCnzGM0E58Mly1CeE+E2qr0JPjm0MPT098EcnENFXf76tTJjRdQ1TgeGk+5mOxoKnqRobAUgEETMsjsCEz48L44vVH7a2NuGtB/fBkaGsmkS07kBlJBKFTVJwy55roSgKohmCrWxtUpVDYLnLFPgcJ6IrVr5IRJcDWG8aXhuApfnig/HXViGiW4noEBEdmpgwL/uPFb/Wtlb4NBMDn+ooAKCtLek/tURh/mwCn2eHRxFR1ayPLYSApot1L/GanJyEpmnr2sdalGrAdq0445Pl0dsA3CqEmDNeEEL4ESsn85aCzYqVvNGRMVRSQ07veXnoFxnHGBmkIyMjWe/39OnTAICqpYFPibCzY/UyfFkiVNptOH36VNb7Z4WnKArsdjtCgQDm53y471tfXbbdyAQ1AqSMZelB4xsieqCA82Ab2NLO7iv902/eh0DEn/h5YmEAvzr37aT7MQKf6TI+1fj9pZTpHoQIBCAUiSKiqpgLhjC7EEj/HsQCqpq+vvK40UgEiiRDWdLciJmPl7qbJ9Pf5McA3EdE/0BEb49/fQrAffFt65HsNzlptEkI8XUhxEEhxMGGhtwu4Flpa2trxZw+Ztr+fOoYFMWG+vrkXd2NwGc2/Y18gSA0LfuThhFMXW/g85//+Z/x1re8FR/58EfWtZ9cbbSMTw58sjwSIkl9DSGEhiy6ujOWyujoKCrk5Oc7ADg09AuMzi02KYqoQfRNH8+4X2Of4+PZP5g8c+YM7LIMt7KYvRCOqhib9ScdX2WTceb0GctKzzBreDwe/OSH92De78OeA1cu2xYOBhJjGMvB0guxTQWbBdvQjEDltLp6ufvf3PhduO3exM9Nld14zyWfSLqfaXUANsWG1tbUtbezOe+Fo1HUVnjQ1ViPU4PDOD86js6GOmxtTb6qcS3HSCeqqpBJhiLFzulqDslALHsc+DRPpuXqLwK4ArETzh/HvwjAlUKIF9Z57EEAS9uMtgNInhPONqy2tjb4oxPQhTkZjj51DK0trSk/RIz6VGoWkc+DW3rgcmSfBWkEPo3g6lr9xV/8BeYX5vHK0VfWtZ9cbbSMT8by6BQRvX/li0T0PgBnCjAfVgY0TcPM7DQ8curmQJX2GtgVV+Lnl4d/CZdSgf944a/S7tsuOeGQ3ZicXNlgObVXz5xBpU1e9jDJHwziwnjylTxeuw3zCwsYHR3N+his8NweD2645U1obuvErksOLtsWDsUCnxUVFYWYGitdIsX3jOVNa2srFMWWyNhcq5lo+o7uQHZJF8+/1ov+iSkAwKWburCzPbsmhrkcI51oNApFkiGTlPiZmY8Dn+bJmMIlhBgH8H9Wvk5E3xdCvGcdx34IwEeI6HsArgTgE0Jkv2aKbQjt7e3QhYY5bSJRU2w9fNoItnd0pNxuZGNqInMmZzgaxdTcAlprq7M6thYPphrB1bUq1AegLUO9GMbYmn0YwA+J6E8BvIzYjd3liHV5/+1CToyVLr/fD13X4ZarU47Z3nD5sp93N16DOw59HDdt+i8Z9++WqzA9na6U+yJVVdHb14d21/LzSIO3Eg3eyqTv8Tpil6hnz55FSwuXgC8VlRUViczOlUIhzvhka7KPiPyIJd+44t8j/rMQQnhTv5UxcyiKgs7OzrSd3bMxow3iik37Mh4LAPQ0WZk37N6+5jnoApDXeT+pxTM+5XjGZyHKsG0EXOPTPOv5F391uo1EdC+A5wBsJ6JBIvozIvogEX0wPuQRAL0AzgH4BoAPrWMurEy1t7cDAGaj68/4EEKHTx1DR5rApxGU1LLI+FwIhTE4ld1NHwCo8ZPXeov6F+oDkAOfjFlDCDEkhLgSwKcBXADQD+DTQogrhBDZd49hbInZ2VkAgEvOPiYgSTKu6/497Gu5MeNYJ3kTx8hkYGAA0WgUlfbszyMVttiN37lzq7u+s+JVWVmZyOxcyQiIVlYmD3YzlowQQhZCeIUQlUIIJf698TMHPVne9PR0Y1Zf+2VZRA/BH51IW98TyK30mkHXBb75y+x6felCrHsln6ZrGPdNwh+Ilafnpe7W4IxP81hWtE8I8QcZtgvEslwYS6mzsxMAMKsOowv717WveW0aqh5JG/g0gpJqFnVPaisrcEVl9su1jGDqemt8FqrW5kat8clYvgghfg3g14WeBysPfn8sKcopZX+eqrBX42D7G7Ma6yAP/L65zAMBnD9/HgBQacv+PKJIEirstsR7WWmoqKhA6GLywEAoyEvdGWOlq6enB4899hiiegg2Kff7uZksOroDS0qv5VCHU5IIf/r667MaqwoBl2t9iTiapsEX8GMu/rnOGZ/W4IxP86QNIRPRpSm+LgPA6V/MctXV1fC4KzCrrr/8q7EPI5iaTCLwmcsjtiwZJ6/1LvEqVOblRgt88hM2xlgpCwRiNyMOsmZZsV1yY35+Pquxvb29ICJ47IvnkSdOnIE/EIQuBEIpaoN5FIkzPktMRUUFwsGFpNvCwQUoim3dtc4ZY6wQEp3d13hfOqPGHgplyvg0Hg6pOXZel6TskjZUXYdnnQ+gNE3Hnq4d2NQYSyjS19klniVnBD7Pnj2LM2e47P96ZIpkfAGxWmPGb9HSaBD/zTPLERE6uzox05u6/Ov4fD8aK1IHMw0z0ewDn9ksdX/5/AVctrk74ziDEUwt1cAnP3FijLHSYQQ+FSl9kCmiBpc1OMqWjZwIhUJZje3r64PHpkBekkm/o60FHqcDI9OzOD04jFv27V71vgqbgt7hYYTDYQ6WlYjKykoEUwQ+Q6EAKioqeEUFY6wkdXV1AYhlbjbaN+X8/unoIBRZSdvRHQC83lgFh4hmTTAxogs0V1evax+60CERQYo3N+LApzWMRJytW7dix44dBZ5NacuU0vRxAP9FCHGTEOImAN8CMA/gBIDftXpyjAFAd3dX2noqD52+DaFo8ovspWbUYbhdHtTV1aUcYzxhi2bR3EjL8QPeeGq33hqfhQp8brQMSL4xY4yVsnA4DACwUfqA4Ree/gCiWhiHhn6R0/4VsieOkUlfby88yvJzSHNNFWRJQltdTdKgJwBU2hUIIdDf35/T3FjhVFZWQlNVRKORVdtCgQWu78kYK1nt7e2QJAnT0bXV+ZxRh9DW1p5xFV1NTQ0AIJLlveZvTr6acuVEMqpYPMZa6boOAiXulzjwaQ1OPDJPpkjG7QDCAEBE1wP4f4gFP30Avm7t1BiL6ezsxHx0GhE9ebH8D1z+eThtmbMoZ6JD6OrqTBvQcjgckGU5q6XuV2zN7Umfsc/11rYqVAByowUCN9qflzFWXqLxmyApw+Kev7nxu1AkO4b9Z3Pav0w2RNXMN1rRaBTDIyOJZkW58MTfc/HixZzfywrDuMZJttw9FFxAZQ610RkzAxHdSUTjRHQixfYbichHRK/Evz6Z7zmy0mCz2dDW2p5Ysp6rWW0Y3T1dGcd5vV7Isoywml0wsauhDjZZxqFzfTh0ri/j+JCqor6+Pqt9pyKEABEtLgvOoR4pyx4HPs2TKYIiCyGMttXvAfB1IcQDQoi/B7DF2qkxFpOopxJdX53PWX0I3T3daccQETxuN6IWPLWK6jqIaN0Znyw/OPDJGCtlRvYFUeaHZUSEd+zMrd+kRDL0LJoZDA4OQtf1RBAzFx6FA5+lxsjoDAZWBz7DQc74ZAVxN4A3ZRjzlBBif/zr03mYEytRXd2dmNVyD3xqQoUvOppYLp+OJEloqK9HMMuGQd2N9ZAlCQe39ODglvT1Q6O6jqimo7GxMat9Zxa7X+LApzU48GmejIFPIjKuVF+P5d1mN1anE1YwxgliWh1c8z7CegDz0emsTjaVlZWWNDeK6gIet3vDLRkvVUbgMxgMYnBw7f/2GGOsEIybkMV8jMIwlqlnyvg8dmEAwcjy5dGyRPDYbbzUvYQYgc1waPUqnXC8xidj+SSEeBLAdMaBjGWhq6sLs9Ex6CK3LuY+dRS60LO6FwWAltYWhCyo8RlUY/Nuamoyfd/MfBw3ME+mv8l7AfyGiH4MIAjgKQAgoi2ILXdnzHKtra1QFBtmomsPPhnvNbJH0/FWVVlSTDqq66jgTIeSYZxoRkdH8cQTTxR2MowxliPj4Y2ANVkYQghkE1M1sjU9tvRZC4osJc20d8mEixcurGWKrACMwGYo6VL3AGd8smJ1NREdJaJHiSh50WHGEAt86kKFXx1ftS2iheALTSZ9XzZNdpdqbW1DMMul7hFVxf3PvJTV2EA0Fvhsa2vLanwqi+drgUAggPvvv39d+2PJccanedIGPoUQ/wjgrxBbInCtWMxhlgD8D2unxliMoihob2tfcyFpAIn3ZvOUraqqCqoF94lRTUd1VZX5O2aW6unpwfve975CT4MxxnJiXCzrQk05JtXStPuOfR5hNZh2/wIaZCnzBfnAwABcNgVKhqyFXR1tcCZp3udRFAwMDPAyuhKxWONz+b8fIQRCQc74ZEXpMIAuIcQ+AP8G4MFkg4joViI6RESHJiYm8jk/VkSMwOWMuroE2zde/Gv88ty3kr5vVs0t8NnR0YGQqmZVfs2uKHjjgT2YCwZxpDd9aZgFVU3sfz0IBCFij1adTieuv/76de2PJZepERbLXsbcWSHE80KIHwkhFpa89poQ4rC1U2NsUc+m7lWd3Y+NPokjw49l9f4ZdRCKYkNLS0vGsbHApwVL3QVQvc4Oeix/eGkBY6yU2eJBRB2pA5/3Hv0szoy/sOr1/a03wy470+5fEypsNnvGeVy8eBEuee2fpx6bgnAkAg40lIZUGZ9qNAJNU+HxZG5GyVg+CSH8Qoj5+PePALAR0arOL0KIrwshDgohDjY0NOR9nqw4GIHL2SS9J96956P4nd3/X9L3zUSHUVNdm/VnoJGssxBNfQ5fyut2wSYrqHK70o5biGqoqa5e92cxSQRdCOhChyRJ684gZclxxqd5+M6elYTu7m74IuNQ9cX6X+3ereis2pHV+6ejQ+js6MzqqUlVVRXCWRaTzkVUCFRxxmfJ2GiBz+npafT29hZ6GowxkzgcDgBYdt5c6Q/3/2/saLxy1evb6g9mbPCmiggc9syBz/7+fniU1Rfurw2NYmw2c9UkY4k81/ksDYmMz9DyjE+j5icvdWfFhoiaKf6BR0RXIHZ/PFXYWbFi5fV64a30YlYdWbWtqbIbUoqGgj5tBF3d2WV7Aovl2eYj2QU+AcBpt2FTc/qmRQtRDZs2b856n6nIkgQBPbEagwN01tho96NW4r9JVhK6u7shoCeWCQBArbsFdZ7sni7NaoPo2dSd1djq6mqomg4tiwZHui4wNDWT1X4jqobq6uqsxrLC22hd3f/qr/4Kb3rTm/DCC6uzvxhjpcflimV9REXIkv1HRQhOlzvtGJ/Ph/n5+aQd3T1OBxxJlravGhd/LzeZKw12ux2KoqxqbhQKxn7mjE+Wb0R0L4DnAGwnokEi+jMi+iARfTA+5N0AThDRUQBfAfBewbU1WBqdnZ3L7kmzMasNZ73MHYj1uLDZbJjLMuMzG0IIzEdVbDYh8ClJUiLj0/iZmY//Xs3Df5OsJBhPvabV3Ot8RvUw/NGJrBobAUBNfDl6JIuaKhFVxenBzCc+Vdeh6npi36z4bbTA58LCAhobGzE6OlroqTDGTOB2x4KSkQyBzyH/OWj64o3V/3vivyz7OZWIHoDHkz7wOTAwACB5Y6O2uhpUx98/7vPjuVfPJd2HU5agSBJnfJYIIoLb7cacfxZ/+5E/TLweCccyQDnwyfJNCPEHQogWIYRNCNEuhLhDCHG7EOL2+PbbhBC7hRD7hBBXCSGeLfScWXHr7OqET1+d8ZlKUJtDUJ3LKfApyzI2bdqEuRwyPjMJqBpUXTcl8CnLCjRdgxYPfHLGpzX479U8HPhkJaGjowMSSWvq7D6rDkNAZNXYCFgMfGaz3N1pt+GWfZmbPxpd4jnwWTo2WuDTwCdYxsqDsaQ4rM+nHfdc/0O4OHsKF2ZOAAD+v2u/AVnKXBYmIoKoqEwfxFoMfKbfX7XHjS0tTUm3ERHcNiWxL1b83G43IHR89rZ7Eq8ZS9+NgDxjjJWqjo4OzEdnENHTNwE0+OLL4tvb23M6zrZt2zCnaqY19/NHoon9rtX8/DxUVYWiKNCEDk2P3S9zEx5r8H2ZeTjwyUqC3W5Hc3PLmjq7G+/JNuOzrq4OABDWMmd8ZsvYl7FvVvw26tICvnBhrDx4vV4AmQOf797zUSiSDRMLscCiU8kuMBUWcxnrVg8MDICI4EpS43Mpu6KgwZu69qNLJs74LCFutxuR8PJM41deegZTU1Mc+GSsACKRCMLhcKGnUTaMjuhGnc9fn78Hj/d+L+V4Y1wuGZ8AsH37dkRUDUE1u94TZwaH0TeWuhGgLxwLWGZ7T5zMbbfdhmeeeQaKIi/L+OT7B2ts1PtRK/DfJCsZPZu6MavlHvicVYcgkZT1UzYOfDJgMeNzo2V+2rKouccYK35G4DOozWUc21m9E5e3vzmn/Qf1zIHPwcFBuG0KpHV+jnpsCkZHR6Cq5i35Y9ZxudyJpe0GIoIsy4nas4yx/Lnrrrtww/XX48tf+lKhp1IWjHtKnxorD3XTpj/ATZvem3L8rDoCiSS0tLTkdJwdO2JNfH1ZLndvrK5Cvbci5XZfJIotmzev61r/E5/4BG644QbYFBtUoXHGp8U48Gke/ptkJaOrqwuz0RHoIreO69PRIbS0tMKeRfdZAKitrQURIZzF07WXzvUhFI1C03XoaZYhhOLL5uvr67ObNCs440Sz0erbG52gGWOlzW63w+P2IKhn7pyeK13oCKpzGcu39Pf3wyWv/+GRR5GhaTrXIC4RLpcT0cjy7DJJkjjwyViBHDhwAIrNhvt/8INCT6UsGIHP2WgskzNTkoQvOormppacA46bN2+GoijwhSNZja+t8KAyxWesEAJzUQ27dmcu0ZYNxWaDqmtQ4/flnDhhDV7qbh4OfDJTff/738ff/d3f4dVXX8Utt9xi6r67urqgCRV+NXUKfzKz2hC6e7Kr7wnEnlhVV1UhlCbj85W+2JK7SqcTMkk4dK4PJy6mrj8aVnXIspwxO4YVj42a8cmBT8bKR11tHRa0WdP3G9R9ENDTrmIQQmBwcBAeE7JA3PEaoVznszS4XK5Vgc+mllZUVlbC6XQWaFaMbVwcPDGX0+lEfV1DYgl7JrP6CDq6cqvvCcSCiVu3bsVsmozP3rEJ/PzI8Yz7mo+qiGoadu7cmfM8ks8t1txIjTdD5MCnNTjj0zz8N8lMdcc3v4knnngChw8fxqOPPmrqvo26KLNq8i7qx0Z+gx+c+OKy13ShYzY6mnNNlcbGRoTSZHyGIrEnbzvaW2BTZFy5bTMu6e5IPV7TUFdbyx9eJcQIeHLGJ2OsVNU31mNBnzJ9v/PaNACgoaEh5ZiZmRmEQiG4k3R0N3znN89C1zN/xnriNUKHhnIvd8Pyz263Q1Wjy16Lxq+b+BzDWP7xMmTzdXR2wKdlDnwKIeBTRxJ1QXO1Z88e+CPqqpWFUU3Dtx9/BpuaGvDGA3sz7mc2HPtM3m1Sxqfd7kBUVxGNL3XPdmUly81GS8CxEkdhmCV0XTf9yY8RvJxJ0eDokpYb8Du7/3LZa/PaJDQRzT3w2dSESJp7sau2b0l8r+k6NF3HIy8fTTk+pOlozrGuC2OFwDeljJWPpqYmzFsR+FRj+2xsbEw5JtHRPc0N929feRkkKfNFvV2WoMgSBgdTr6xgxcPhcECNLg98qmoUkiRxAIaxAuBrO/N1dLTDp2UuvxLQfYhooZw7uhv27NkDTdcxtyLr0ybLeO91V2a9n9lwFJUVFWuex0p2hx1RXYWqccanlThb2zwc+GTmij+N0nXzGgMZqqqq4K30pl1WINHyf9Iz0Vh2aK6Bz6amJgRUNatsv18fO4WByWns6059jLAu0NTUlNMcGCsEvnBhrHw0NzdjIToDTUQzD87BnDae2H8qRnZmuoxPjzN2M36k9yJeeO18ynFEBI+icOCzRNjtdmgrMj51VYXNZuPsFcYKgLPxzNfe3o6gOoeQPg8gltkZ1cKrxhkNkNYacDQyNGeS1Pm0r3iQNOlP3czQF1Gxe88e01Yf2u12qLqGaHypO/8bswafM83DgU9mrvgvpxWBTwDo6OzMup4KsLgsPteTTXNzM1RNh5rFErw37N+D7sZ6tNUlb/IghEAgqqa9QWSsWHDgk7Hy0draCgGRc23sTHzqONwuT6JzfDJDQ0MgIriU5IHPpUvcj/b149JN3WmP6ZKJa3yWCEVRVi111zQVslye2Z4/+9nPAACapmFoaGjDlchhxW+j1dbt6+vDxz72MXzhC1+w7BiJzu7RWGDz9MTz+PGp21aNMxogrTXw2dzcjPq6usRS9XSeOHEm6esRTcdcJIpLLrlkTXNIxm63IyrURI1PDnxag8vkmYf/JpklrLro6+hoh19Pvazg0ODPMReeTvzsU0fhcrpRW1ub03GMIGUwRZ3Pi+OTePTwsaz2FdJ0CMEZn6w08JNFxspH4sZMza4b+j2v/CMCEX/GcbPqCNo72tN+XgwODsJtUyClGHP7z3+NYxdigcw/fv11sKUIkBpcioLR0VFoWur626w4yLIMIZY/ANd1DUqG/8el6qWXXoKu67jtttvwnve8B/fee2+hp8TYMhstKHXvvffiqaeeQl9fn2XHWHl+3dV4Nd69969WjfOpo5BleV0JMJfs2wdfJPO5792vuzzp60bQdM+ePWueQ2Jfs7M4depUPONT5YxPi/F9mXk48MlMZQQ8rboxaW9vx1x0ClF99VICABAQWBpy9aljaG9vy/lDI1Pgs6uxHm/KopD00n20cI1PxhhjeZSojZ2iKaDhl2e/jX964r/g2u7fgctWmXG/Pm0YnZ3pGzUMDQ3BmaJ+56tDI7hh9/a0TQFX8thkaJqGiQlzs1eZ+WRZhq6tDHzqkKTyDHz+/d//PSRJgs/nAxALDDBWTDZaUCoSiUBRFEv/3G1tbQCA2SUPFof85/CLs3cvG+dTR9HU0Lyu+sZ79uxBIBpNeV+ayWw4AkmSTOnoPjg4iBdffDGW8RkPfHL9ZlYKOPDJTGUscbcq8Nna2goA8Mfri610efub4HUsZnf69VG0tbflfBwjSBlIc4LJNpjKgU/GGGOFUFVVhSpvNaaj6ZeIX931Dvy3yz+PzuqdGc9tUT0Ef3QC3d3daccNDQ3CnSLDr7HKi476urTvX8nY1/Bw+iAuKzwiWvEYOvZgvNwTV4wmFNyMghWbjRb4NBJxrCw74XA4UF/XsGxFRZ27Fbsbr1k2zq+PoqNrfQ2F9u6NJdvMJqnzmY2ZcBRbt2yBy+Va1zyAWBD2j//4j2OBTy2KiBaFw87Ns1jx48Cnhebn5ws9hbwzAp6qqmYYuTbG0zW/OpZxrC50+KITiWBpLrxeL1xO55qfrC0VUDUQEdf4ZIwxlnebNvVgWk0f+KywV6POk91DwulorMFQT09PyjELCwuYm5tP2diopsIDrzu3GzCjVqjRNIkVLyICNmCZSyPjaaMFmVjx22jZePkIfAJAR+fyzu5OxY22qq3L5hFbfbi+wOfWrVtht9sxE8q9UaEuBPwRFXtNrO8JxAK/US1W45M/81gp4MCnhf7rf/2v+NnPfob3/P7v4bHHHiv0dCyn6zrUeOAzGjW3g6whkfGpJs/4XGpBm4Yu1ESwNBdEhNbW1rQZn9kKRlXU1dbySYEVNePikGvJMFZeNm/ZjOnoAHSRXdPB+cgszk6+nHL7ZPRibL+bN6ccYwQnXSbebDsVGUTEGZ8lQNd10IqGDESEcu/5Y5xH+XqPFZuN2iDF6sBnW1sb/FrqZJyQPoewFljTvehSiqJg186dmI3knlg0F1Gh6noia9QsiaXumgq7nRujsuK3MT8F8+SBBx7AhQsXMDQ8guPHjxd6OpaLRCJJvzdTdXU1nA7nssBnMDqP+4//y6qxxpi1ZHwCQFt7O0ImdKcPaDpa13nCY4WzUQKBxp+Tu9EyVl62bduGqB6GTx1JOcYXmsToXB+EELj75b/HyFxvyrGT0T64nO6059aRkdixUi11XwuJCG6bwoHPEqBpGoQQCIWCidckSdowjak2WgdtVvw2WuAzX9eybW1tCKg+RPRg0u3GMvi13osutfeSSzAXiULTc/uzzcSXx5vR2Gip2FL3WI1POy91ZyVgY30KFoCxtGAjLDEIhxcbDoVCIUuOQURoamrGnLbY3MChuHGg9eZVY40xa+2m3traikBUXffJM6Tp617iwAqHA4GMsVK2fft2AMB4JHkw87n+H2PQ9xouzJwAEeGtO/47ru/5vZT7G4/2Yvv2bWlvpI3gpJmBTwBwSoRhXupe9FRVxfT0FF546peJ12RZgapZUwap2GyEa/5SQkR3EtE4EZ1IsZ2I6CtEdI6IjhHRpfmeIytNRianL0UJNiPwacZ94O7du6ELAV8kt1WVs+Eo6uvq1nw/nIqR2R5Uw7DZOOOTFT8OfFrM+FDYCFljwWAw6fdma2ltxpw+mfhZIglb6lZfo8yp6wt8trW1QdMFwtrasz41XSAYVU150sfyiwOejLFy0NXVBYfdibHIuaTbVV3F5rr9uKrz7QCAnprUWSGaUDEZuYBdu3elPebo6ChssgybbO5lpkuRMTySOnOVFYdIJIK29k7c8IZ3JF6TFRuiFq0GYiyDuwG8Kc32NwPYGv+6FcDX8jAnVgYWA5+jSbf74qsPzWhwu3v3bgCLGZzZ8kU10+t7AkgEO0NqmMt7MNNZcR/OgU+LbaSlBYFAIOn3ZmtqasK8Nplx3Jw2heqqGjgca0u/N4KV66nzabyXA5+lZ6MsyTNshIcz5SpTNgvb2BRFwY6d2zEWPZt0+3Xdv4tTY8/glZHHM+5rItIHTajYuXNn2nEjIyOJZkRmcikyfD7fshUmrPiEw+FVSx9tdjtUVbWs+SVjqQghngQwnWbIOwF8W8Q8D6CaiNYfqWJlL1PvCb86hvra+jXfiy5VU1OD1tYW+MLZZ3yGVQ2BSBS7dqV/WLkWRrAzFvjkjE9mDivvRzdOVI5ZbmkX+/m5OcuO09DQgKA6B1Wkf+K1oE2jobFhzccxnuIFomu/SA/EL/A58Fl6+OaMlZC7kT6bhW1we/fuxUTkAqJ68oBhd80eDMyeweGhXybdbhiNvAogc62w0ZEROKXsLl7PDifPlEnGCKaOjaVuJsEKLxgMwrYq8OlIbCtXxg0bP0gsOW0ABpb8PBh/jZWofP0OVlZWosJTmTrwqY2jtc28e8A9e/bCH02emHFuZAz6ivqfs/Fl8Ua2qJmMkh4hNQwbZ3yyEsCBT2aahYUFAEA1rA98AsCCNpN+PvoUmpoa13ycpqYmENG6Mj6D8feut5sfy79oNLcaOowVShbZLGyD27t3L3ShplzuXutuwS1b/gh7mq9Lu5+R8KtoamxOnIdTGRsfgzNDxqc/EMQDz76E/sns/+k6OfBZEubn52F3upe95oj/bFwrliMukVOykkXJVv3PJKJbiegQER2amJhI8hZWbPIRAG1ta03Z2d2vj6Gt3bx7wJ07dyIYVRGK31+e6B/E06deAwAMT89CW9GU1xeOQpKkRK1vMyUyPrUI1/hkJYEDn3myEZ7+zs3N4dSpU/Dq+rLsT7NlH/icQX19/ZqPY7PZ0NjQkDLwefvPMi8LDEQ1OJ1OVFdXr3kerDA48MkYKxd79+4FEWEkfDrlGJetAnY5dTdqIQRGomew/8C+tMcKBoNYWAjAmaG+p9ftwlsO7sPrL8l+CZ6xz/Hx5Nk1rDj4/XNwOF3LXnO6PABi14rlyrjW5wBoyRkE0LHk53YAwysHCSG+LoQ4KIQ4mOnhDysso8luPkrOtba2YE5fHQjXRBQL0RlT6nsajDIzxnL3PZ3tuHbXNgDA9bu3w7bigaMvEkVPd7cpS+1XkuXYsSJalBu6sZJg6acBEb2JiF6Nd8n7RJLtNxKRj4heiX990sr5FMJGugiam5vDtm3bUC/JmF9YsOzPXFdXByC2lD0VTagIqnOJsWvV2taGoJq8udEH33RTxvcHVQ0tLS0bIvBdbowacrq+9uZWjBULzlTZ2LxeL3p6NmEoTeAzkxl1CAHVhwMHDqQdNzkZq8GdKeMTAFxLlsfNzC8gkKF2pzN+o2UcgxUnv98Pl7ty2Wsud0ViW7kyrnv5mq/kPATg/fHu7lcB8AkhuItaCfv1r3+NcDicn4zP1lb4o+MQYvn9wpw6CQFhauBzy5YtkCQpq87uQgj4oxp2WlDfE1hc6h7RookgKGPFzLLAJxHJAL6KWKe8XQD+gIiS/eY9JYTYH//6tFXzYdabn5+HoiioJUDVtMTTNrPV1tYCAAKaL+WYYHybMXatWltbEVpH4Cuk67zMvUQZ/36t+nfMWD5xpgq77LJLMRp9FZpYWzb7UPgUAGQMfE5NTQEA7Fl0dBdCQI03knv5/AU8fix9YFaWCDZZxvQ0V3YoVkIIzM7OwFPhXfa6O/7z7OxsAWbFNjIiuhfAcwC2E9EgEf0ZEX2QiD4YH/IIgF4A5wB8A8CHCjRVZpJ3vvOdcDgceQnINTc3QxMqAvry+1K/NpHYbhan04nOzk74I5n7EIQ0HRFVs2SZO7CY8SkgOPDJSoKVeclXADgnhOgFACL6HmJd805ZeMyis5Ge/s7NzcFOBE/85/n5ebhcrrTvWQuv1wuJJAT11IFP4+RTU1OzrmO1tLQgFFWh6QJylo0aDEIIBOIZn6z0BAKBZf9ljBXGRlgxkQ+XXnop7r//foyGz6LNmXsGyGDoBBrqGzM26/P5YudfRxZLDC+MT6J3bAKvv2QXbtizA9Esmso5ZImDZ0Vsfn4e0WgUHm/Vstc9lbGfjcB4OdpIq7xKiRDiDzJsFwA+nKfpsDwwAnH5CMgZ93l+dRy+hSm4bV5UuxoxF294ZPZ94I4dO/DE0GDGcf54VujWrVtNPb5h6d8tBz6ZWaw8f1q51D3bDnlXE9FRInqUiMxvOcbyZn5+Hk6SYFQIs6qOkyzLqKz0IqilXi5lbFtvbc2mpiYAQEhL3eBoPhjCyMzsqtejuoCq6aY+6WP5YzRgWFjgwCcrbsmyWQo9JytshAeIVtq3bx+IJAyGT+T8XiF0DEdP4uDll2X8/2AEPm1ZZHz2NDUkanzaZBnuLOqQKURlvVy61BmlNCq9y1fcuNwVUGx2cKkNxpjV8hn4NO4V57RJDPhexWRgKPGzJEnr6jeRzJYtWxCKqginuTcFAH9EBRFh8+bNph7fsLR+aj5qqS61sLBQ1g/RmDXX/FZmfGbTIe8wgC4hxDwRvQXAgwBWPZYgolsB3AoAnZ2dJk+TmWVhYQELfh90lxuQJUsz5aqrqxGcSH3jE9LnEuPWwwhaBlUNHlvyX5f5UBhT/nm01Cw/ltFxzzghstJiBO7n5vgGmxW3TNksjAGx1RLbtm7D0MUTAH4/p/dORvsRVOdw2WWXZRxrNDe05bhKIlsKAXMc+CxaY2Ox7saV1csDn0QEb3VtWTem4kxPxoqDUX8yX0vdAWBOncCVHe9KvD6nTqK+rsH0xj9GIHMuosLhSv3nm4tE0dLSbMnqS2B5YCqfgU9N0/DWt74F09MzePbZZ1FRUZG3YxfCRj2vWPHntvJfacYOeUIIvxBiPv79IwBsRLTqsUgp1ybbSMteAoEAQoEAtPhSNSsDn1XVVQjrCym3G4HPqqqqlGOy0djYCCAW+EyluaYKe7raV70ejD+JM/bBSosvfmPt95dvB1rGSslGOI9a7eDll2E0chYRPbfaxYOh4wCQVeDTqIssW5ShK0uEQDBoyb7Z+o2MxHrCVNeuvvbxVtdjeHhVs+yywVnpjBUHI+CZj27jbrcbHncF5rXlGYhz2iSam81Pflka+ExnQdOxefMW049vWHpNls/rM1VVoaoavF4vZmZm8nbcQtEyZPay7FkZ+HwJwFYi6iEiO4D3ItY1L4GImil+lUBEV8TnU5Z5yxvhYigUDGJbYyNanLHF7kELb0yqqrwIYz71XPTYtvU+BTIC7WEt9wZHoXg3eA58lqaZ6djJ1O/38UmHMVYWDh48CF1oGMmxu/tg+AQ62juRzcPnaDQKIrLsukciQjQSsWTfbP1OnDiBV197DRXe6lXbauqbMDg4lP9JMcY2FCPwma9MxKbGRsyry0MYC2IKjU3m3wPW1NSgoqICC9HUgU9NCCxEVGzatMn04xsKFfhcGsy22Wx5O26hqFnUPmfZsezTQAihAvgIgJ8DOA3gPiHEyRVd9N4N4AQRHQXwFQDvFZzSUbJCoRBsAIyPoHA4bNmxPB4PImkyPiN6AG6XZ91LHOx2O7yVlYll67kIaxqIaN0Nllj+CSEwMzMNh9MFXdc3xBNFxlj527t3LxTFhoHw8azfowkVw5HTOHh55mxPIJadYNEqdwCxOko6XyoWrbm5OVx/4y1JA9+1Dc2Ym/OXfXOqjZDswFgxM34H8/W72NjciAWxGPgUQmA+Om1J8gsRoae7G/Np7k0DURVCCHR1dZl+fMPSkI2u554gtFZL7+0dWdQFN5NV/UvSiUajeT9mubL0MYgQ4hEhxDYhxGYhxD/GX7tdCHF7/PvbhBC7hRD7hBBXCSGetXI+zFrRSAQyFgvHWvmLWlFRgbCeeil9WA/A4/Gk3J6Lurq6NWV8hjUd1VVV3OlunQrxLGRmZgaqqqKlI7achJsxMFZ4HExYP6fTiT27d2MocjLtuIdOfzXx2TseOYeoHspqmTsQuynRs/jYDkejCIZzz9wUAOQ8N1Jg2btw4SJqG1qTbqtrjL1+8eLFfE4pb4zfGc7hYKyw8v072NDQgAVtMUkipM9DE1HTGxsZOru6EExzb7oQjQVFreyNsjQTsVAr45xOZ+ZBJllYWMDll1+OP3r/+/Ma6LUykWyj4StHZhpN1yFh8R+VlR+CbrcbES2U8sQWESYGPuvrEcnmLm6FsKajrq7OlDmw/BodHQUAdG7eAWCxZhljrHA4mGCOSy+7FJPhC4mSMMm0VG5OBJoHQ7Eg6YEDB7Lav91uhxAiY1Zm39gEXh0ehRACJ/oH8drQKM6NjGXcvy4E7HnO8mDZCQQCGBkZRkPL6rrnANDQHCv939vbm89p5Y3xO8PlcRgrLCMwla/fxfr6egRUH3QRO54RBLUq8NnR0YFQVIWq69B0HeqKP2cgHpRsb0/+WWyGSCSC/v5+zPvnEClQ+Zl8ZnwODw+jvr4efb29lpbzW2mjBj6tSHbgwCczjxAgECbDYfT19Vl6k+rxeCCgQxXJPwyiIgy325wudrW1tYiu4c8S1QVqamszD2RpFSLLa3BwEADQs20vAGBoiGuSMcbKw6WXXgoBgZHwmZRjLm9/U+L7ofApbNq0OetmgcZDRzXDA8Md7a3Y39MJIQB/IIjGai8aq7wZ96/qwrQHm8xcRkCzsSV5lpG3ug5Olwfnzp3L57TyzmjwxRgrDGPVYb7qI9bV1UFAIKD5AAAL2nTidSu0tbUBAAJRDacHhnGkd3kWfSCqwVtZaWnH80gkgo6ODnQ0thUsOJfPbvJL/4z5/PNa2Sx6o+HAJzMPEQQEaux2tLa2WrrE20htV0XyJ0yqCMFlUuCzqqoKkTUsdY8KgerqalPmsJEVIstrYGAARISm1i5UVFZhYGAg73NgjMUYnwG81N0cO3fuhE2xYSh8KuNYTagYi76GSy/NLtsTQCJAGslyKZgkEV63YyuqPW54szhvRwW4dnaRevXVVwEAzW09SbcTEZrauhLjylWhsp8YYzHG72C+AlRGgDOgzy77r1UZny0tLbHjqBr2dLXj8q3LmxgFVA2trclLjpglGAyCiOB1VCAYyF8GZKEs/VzPZ+BzYSF1T5NyZsX9Pwc+mWmefOopBFUVgggOhyMvgc+oSP5UXUXYtPR3r9eLqKbl3EwhqutZZ8iw4tLX14ea+iYoNjvqmtrR19dX6CkxxpgpHA4HduzciZHI8ozP5/sfxk/O3L7stcnIBUT1MC655JKs92/c6IWzaAp49MIAfvj8oaz3DcTKyFh1M8nW59VXX4W7ohKV1alXuzS39+DcufNl2bDBWFbLgU/GCsvIkstXtlxtfIVfQJsFAATjmZ9WPaRrbm6OHSfFeTasCzTHg6NWMQJyNQ4v5udTl84pF4XK+CxEQ6VyxYFPZprfesMbQIoCY1GB3W637FhGUDN1xmfEtILHxjKBTMv2lhJCIKJqli4x2Gjyme119uxZ1MdrkTW2dqK3t5drdjFWIMbvPtf4NM++fZdgItIHVV88h17V+Xa8bccHl40zgqO5BD6XZqJkMh8MYWTah3MjY1gIZb6RiOo6wqqauOljxeXkyZNo7dyS9nzd2rkFqhotq+Xuqqri17/+deI6ga8XGCssIyi3kKegkbHCL6j7AQAB3Qe73QG3223J8aqqqmC32xFK8lkjhEBI1dDU1GTJsQ0+nw9OxQGvowJ+v9/SYxWDpcHOfJYzmZ2dTXxfjg8M84kDn8w0FZWViBLBuI2y6sMeWAyqpgp8aoiaFng1gpfRHDq4qfEbdK5DZp58BT3m5uYwNDSElvbYUr3mtm6Ew+Gy7ULLGNt49uzZA11oGI+mbzIzGj6LxoamnDIsm5qaYFMULERT11YzmjFcs3MrPvTmmxGKRqFlcY419mllp1q2Nn6/HxcvXkRb19a049q7Y9uPHz+ej2nlRTgcxksvvZRoqMIPaRgrLL8vlnHp8+UnIGdkdhqZnkHNj+qqasuOR0Soq61FOEkpNlUXUHXrV0bMzMyg0uFBpd0Dv99fkAc++fqsHRgYWJbVms8A5NTUVOL76enpvB23HHHgk5nG4/EgTATjeYiVgU+bzQYAie55K+lCTYxZL+PPocU/XIORCOYzPOnR4tmhVv4dMGucPBnrYNzauTn2364tAIATJ04UbE6MbWScPWW+nTt3AgDGIumz7ibU89i1e2dO+5ZlGV3d3fBHUgc+lzZjICLIJMFuUzLuey6+z02bNmUYyfLt2LFjAID2nu1px1VW1aK6tgFHjx7Nx7TywuPx4OMf/zjXIWasSPjiWXL+ufwEPl0uFxTFhpAeyzAN6XOorra23FlDQ0PSwKfxmlWNlQwTExOoslei2lEJXeiYmZmx9HjJ5KusyH333YcLFy4kfs5X0ywAGB8fT/p9udJzSDTLFQc+mWkqKysRIkIQIvGzVYygpiaSP3HRhApFyXwTlQ1jWb0RzBycmsH5kfQfPEaQ1Kzl9ix/S92PHTsGSZLQ2hkLeNbWN8NT4S2rmzTGSolxYctZVOapq6tDfV09JiKpMz5D2jx80fFEkDQXO3fuxJyqpfx/trIZQ0TTILIoJ+MLR+F2uxMdbVnxOHr0KGRFSTw0TKdj0w68cvRo2f1OG7XtraxxzxjLzBfP+AwEg3nJziMiVHmrEDQCn2IeVRYHPmvr6hBN8hEajgeOrG4CODo8gjpXFepc1QCAkZERS4+XTD6WnB85cgQf/ehH0dOz2LQvn+eukZER2B2xxo/Dw8N5O26hWNnMiQOfzBSjo6P4/ve/j4Cuwygj7fV6LTuecVEpkPypgC408wOf8Q+5rS1N2NeTfpkdBz7Nl6+TzKGXX0ZLxyY4nLGTDBGhY/NOvPzyy2V3k8ZYKQgGy79baCFs274Nx4efwk/P/EfS7ZPRCwCArVvTL11OZteuXYioGhayqPMJAPu6O+ByZC5P44uq2L17N2fWFaGXDx9GW+cW2GyZ/z92bt4J3+xs2TUONK5NraxxzxhLLxwO4+z589ASy919eTmu11uJsB4L2kTEvKX3wUCsrmgkScan8ZqVgc9wOIzxyQk0umvR4Ik1dhocHLTseEstzQjMx/Xh9773PSwsLECSFsNm+Xy4dfFiPzbtuAREEvr7+/N23EKxspkTBz6ZKZqbm/HRj34UUSHgi8eGrPzAN4Kamkieaq4LzbQPJeMCNofeRokO8GYFXzeyfC5z9fv9OHP6NLq37ln2es/WPZicnCy7mzTGSoFRV4mDXebavHkzXDUy3rT9T5Nun4rGLrC3bNmS87737dsHAJgOmbcMLaLp8IejiX2z4uH3+3H2tdfQtWV3VuO74+MOHTpk5bQKhgOfjBWOz+dDc3MztsbvQ5c2h7GSt8qLkB67Xgnp85aufARi99kRVV2VlGH0pKiqsi7jtL+/H0IItFQ0oMFVA0WS83aPFI1GMTExAU3T8rLU/XOf+xwqKiqWfa7n6zN+fn4e4+NjaG7rRm1DM3p709dlLwfG76sVDbM48MlMU1sbe+IzAQGXw2Hph8LiDXDyaKQu9GVPZtbDCF6KFMdKxjgHmVVndCNb2kXPai+++CJ0XcfmnfuXvW78/Oyzz+ZtLoyxGCNbgzOuzbVp06ZYXa5o8uVp09FBVFZ415Q10tHRgbraWkwFzbspMYKol156qWn7ZOYwVkT0bNub1fiq2gbU1jeXbeCTV/sUFyJ6ExG9SkTniOgTSbbfSEQ+Inol/vXJQsyTmcPn80FRFDRQ7D4wXx3HKyoqEEUAQgiEtYDlgc/KykoILK4yNKjxwKeVx3/11VcBAB2VLZAlGa2VjXjttdcsO95SSxNi8pkcs/Rz3VgNajXj77SprRtNrV04E/97L2eTk5Px/06Yvm8OfDLTGBmeE7D2KROARFAzVTBSQJgW+Ewsq8/hntsYatYcNrJ81G8xPPnkk/BUViXqexq81XVobu/Bk08+lbe5MMZijI6W0Uj+HoJsBEZn9Fk1ec2oWXUYXd2da8q0JSJcceWVmI5ETQtYTwTDcLtc2LVrlyn7Y+Z58cUX4XC6sqrvaejetheHDx/Oa3fcfOHVPsWDiGQAXwXwZgC7APwBESX7EHlKCLE//vXpvE6SmcpYKltLy3+2WkVFBSIiAFVEoAsNHo/H0uMZ+4/qKwOfsXtgK4Nzp06dgtvmRGN8mXu3tw2nTp3KWyCyoaEh77WUlyZ05SvwaTTWbe3cjNauLZgYHy/7BkdDw0MAgJHRUdMTHjgqw0xjBDunYX3g07gRS52FKUxbFrl4rOwZv6gc+Fw/44LF6myvUCiEZ599Ftv2HEz6/2373stx6tRJjI2NWToPxthyo6OjAICxsTHO+jRRe3s7AMCnjibd7tNH0dHRseb9X3XVVYioGmbD2QW2omlumIQQmApHcfkVV3BQqcgIIfD8Cy+ge+seSDnciG7afglCoVCiGzxjFrkCwDkhRK8QIgLgewDeWeA5MQsFArFuE9WI3b9Z2SxlKbfbjYgeRETE6k5aHfh0u90AYoHOpVRdwOV0Wloe6MjhI9hc3QEpnlW7taYLgUAA586ds+yYhqV/rnwGP5ceK1/HPXz4MBpbOuByV6Bz087Ea+VKCIGB/gGQRAgFQ4nEB7NwVIaZxkip1wBUWFzQOdNSd2uOxQohX7V5nnnmGYRCIezaf3XS7cbrjz32WF7mwxiLGei/CAAIBEOYmZkp8GzKh8fjgbfSC7+6OntAE1EsRGfQ0tKy5v1fccUVkCUJ48HMmbpRVcM9v3ku5XZ/REUwquKaa65Z83yYNfr6+jAxPo5NO3Krvdq9dTdkWcELL7xg0cwYAwC0ARhY8vNg/LWVriaio0T0KBElLVZLRLcS0SEiOjQxYf4yTGYOI/AZDoUwOzubtwaJbrcbES2IqB5bqeZyuSw9nrH0Wl/xQFgTwtJjj46OYmBwADvrFjP8d9RtAhDL/rdaIQKQwPIEnKUNlqwSCATwyitH0R0vIdPU2glPhbesz5nDw8MIhULYdLALAHD27FlT98+BT2aapR+yxlOocmB8uOUU/jSyRMsoO6lQfxaj1ke6bCAzPProo/BW16Fz046k22vqm9DWtRWPPvpoWf1/NZTjn4mVPlVVcfHiRXRWxH7/z58/X+AZlZfGxibMa6ufqC9oMxAQaGpqWvO+KysrsW//fkxk0eDIpsh4++X7Me5LXottPBACEeHqq5M/mGKF8/zzzwMANucY+LQ7nOjYtB3PPps64F2q+IF5UUn2P2PlBc9hAF1CiH0A/g3Ag8l2JIT4uhDioBDiYENDg7mzZKYxymfYELu2zVevAJfLBU2oCMcbHOUr8LmyxqcmhKV9Np5++mkAwN7GbYnXqp2V6KxqxdNPWV8SbOmqvHyurFxaei0fZdief/55qGoUW3fF6pqTJGHzzv147rnnyrJEDACcPHkSALDjui0AxUoqmIkDn8w0heh2lg9rCXwaY/PxRChf8llAeqnh4Vj9uZnpacvmMDk5iRdffBF7LrsWlOYkuvfy69HX14czZ85YMg/G2HJ9fX0IR6J4fXsseGb2RdBG19jUgICYXfX6vDYNAKirq1vX/q+77jrMhaNYiKoZx86HwpgLJr+ZGA9FsXv37jU1WmLWeu6559HY0gFvde7/Vjbv2I8LF/oS5SzKBT9ILCqDAJbW7GgHsKywsRDCL4SYj3//CAAbEdXnb4rMLEeOHEmsFGtzOlFTU5O3+xcjEBnU/ct+torRQHdl4FMXAnYLa1D++rFfo7WyEc2e5b8ilzbtxMlTpyz/PC/Ug6WlTbLyUTf2scceg6eyCh1LEnJ27LsS8/PzZZv1eeTIEThcdjRtrkd9R63py/o58MlMk88nMIvByFTHIdMuPI0TZi6fs8ZQVc18s1cq8tlkaKlz8QwvNRrF0NCQJcf46U9/Cl3XcckVN6Qdt2v/1bDZ7Hj44YctmQdjbDnjoufSxig6KgVefrk8u0AXSk1NDYK6b9XrQS32mhmBTwAYC2TOuOlsqMPm5sZVrweiKvzhCK6//vp1zYWZb2FhAceOHcXmHfvX9P7NO2PvM7JGSx1nehallwBsJaIeIrIDeC+Ah5YOIKJmiv/PI6IrELs/Nre4HMuLpYFPYxF0vpJQjEBnSJ9b9rNVjMDnyvtdXQB2u82SYw4NDeHY8WO4onnvqm1XtMRe+/nPf27JsQ1LA9n5TDCamZmBpmnQNM3yskuzs7N45plnsOvA65bFVHq27YWnwotHHnnE0uMXgq7rePa5Z9GyowmSLKFtVzOOHz++LOC8Xhz4ZKaJRBaXs1mdgr0Y+Ex+kUkg0z4MjeBlLhe0UnxsobIkrTA/P5/3Y4bDYfSePw+1K1ZH5vTp06YfQ9M0PPTQw+jeuhu19c1pxzpdbuzcfzV+8Ytf5K1YOmMb2TPPPI22CoE6p8AltREcO3qsIJ9F5aqqqgpBdfVFZTB+47beRoXNzc3YvGnTqjqfv3jlBPrGsquRZ7zXCKKy4vHSSy9B07READNXdY2tqK5rxHPPlcdydyMAwQHQ4iGEUAF8BMDPAZwGcJ8Q4iQRfZCIPhgf9m4AJ4joKICvAHiv4LTdkvSnf/qnaG1tBZBjiTITGJ2+Q/Gl7lZ3/jYa/a38l6oLAUWxJvD5k5/8JFZ2pm3/qm0N7lpsr+3BT3/yE0sDkktjDEtjD1abmprC9PQ0pqenTW+6s9IjjzwCVVWx74obl70uywr2HrweTz/9NMqtzvDJkycxNTmFrv2xxptd+9uh6zqeMrF8Agc+mWmWRuT9vtUZJGZavB5JEfgk8zI+jQ/YXH5ZjGvecsr4zFeToaWOHz8OVVURueQykNOJl19+2fRjPPfccxgbG8WBq2/Javylr3s9QqEQfvazn5k+F8bYosnJSbxy5BVc3hgLfF3RFIWqafjNb35T4JmVj8rKSmhChaovv3mI6LEHOxUVFes+xvU33IDZUAQRbfFG6JZLdqOnKbsaeeOBMDo6OtbVYZ5Z4/nnn4fT5UZ797bMg5MgImzesQ+HDr2ctzp8bOMRQjwihNgmhNgshPjH+Gu3CyFuj39/mxBitxBinxDiKiHEs1bNpRDX0htNoWLWRpm3cPz8aXXZNyMTMNmf1oqmP+FwGA8/9BAuadiOWlfyh6LXdxzE8MiIpVn8+a61aZienkbjlq1oaGqyNPCpqip+8IMH0Ll5JxpbVl/3HHjd6yGEwA9/+EPL5lAIjzzyCGx2JRH4bOiuQ1WTudmtHPhkphkbGwMA1AAYGR5OP3idjCdJEiX/J0yQTDvxGcFLaQ0Zn1Y8iSrUCd34/5tPTz31FEhRoHX2INK1GU8+/bTpweQHHngAlVU12LbnsqzGt3ZuQWvnZjzwwANlWcerHP9MrDQ98sgj0IXAdS2xh09bqjS0eAQeeujHBZ5Z+TAaEUbE8q63EREEEZnSnOF1r3sdBICJJVmfkpTd+VTVdcyEo7j22mvXPQ9mLiEEnnvueXRv3QNpHTfZm3fsRzgcwrFjx0ycXWFQGTa2ZOb64Ac/iHe967fx53/+obLqA1BMjN8/4w4xX3/PRqAzose6ylud8bkY+Fz+eSOQW3m2bP3iF7/ArM+H13dflXLMpc27UOPy4nv33mv+BOKCwdj1iq7rie/zYXZ2FprLA3K58a1vfcuSZBwA+PWvf43x8TFccf1bkm6vqWvCtr2X40c/+lHZrD70+/34+S9+jk1XdMHujGUrExG2X7MJR48eNa27Owc+mWnOnTsHArAbhNHxcUt/GbPJ+DTrRGcEL3MKfMb/a8WS/0It87x48WLi+3w8sQ6Hw/j5L3+JSPcWwGaHunUX5v1+U58iXrx4ES+99BIOXH0LZFnJ+n2XXfMG9Pf349Ch8qs3yDdsrBhEIhE88IP7sadOQ4snXtqEgFvaQzh58hROnDhR4BmWB6MGmSqWZ9upegQOu8OUJbvbt29HdVXVssBnMv5AEM+9em7Za5PBCHQhuJt7Eert7cXU1OSa63saOjfvhKwoZVHnk5e6s0z++q//GpOTEzh+/FjBaueXO+O+zQYgHAzmLThm1Nw0HiQaS9Gtku5zhlIkBq2Vpmn47ne+i86qVuyo7Uk5TpFkvL7zKhw+ciTRodtsxv/PU6dO5aXJkGFufh663QHYHeju7sall15q+jE0TcO3vvUtNLZ0YOuuAynHve7md2B+fh4PPPCA6XMohB/84AeIhCPYffP2Za9vv3YLbE4b7rnnHlOOw4HPPNkIwYSXDx1CkyRhW3yZ+ZEjRyw7VqbmRhIkCwKf2b9HtjDjc2m3vHwuDVsaaLDqZLbUr371K8z7/YheEsvEVDdtAyoqcd/995t2jAceeACyouDAVTfn9L6d+6+Gp7IKP/jBD0ybS6EZn1GBQKDAM2EMePjhhzE1PYO3dy+/MbypLYIKO+Guu+4s0MzKi5GhoonlD+k0RGC3mbNMT5IkXHnVVZgOR9NeCzntNrTX1S57bTIUhtPpxN69qxspsMJ68cUXAcSaLayH3eFER892vPTSS2ZMqyhshGt+tjZLG5VYsRw5lXLqOZDJ3NwcCIAdwPTkpGWNUVcyAp3Gg0QjEFoOHnvsMQwODeItm67L+GDnhs7L4bG7cffdd1syF+OBwZ49e/JaUi4cDkMoCoSiYGBgwJJjP/bYY7h48SJed8tvg9I0im7p2IQtOw/gnnvvLfm69z6fD9///vfRtb8dtW3Vy7Y5PHbsvGErfvXYr9Db27vuY3Hg02Ib5env5OQkjh49ih26QBcAJ0l47LHHLDuecQJPudSdJNNO8mvK+LQw8Hnu3GJGjBkfAtkIhUI4fPhl7N5bB5tNtrwRgaqquOvuuyEam6F1xJ8sShJCB67E4ZdfxtGjR9d9jIWFBTz66KPYFQ9i5kJRbNh/5U149tln83ZBlS9WF+xmLJOFhQXcfded2FmjYU/t8gtLpwK8vSuAF1540bJlRhuJcWOmieXnS01oUEy8aTt48CDCqoa5SOobBbuioKN+eeBzOqzi0ksvLasbyHLx0ksvob6xFd6aunXvq2fb3ngGaXmcfzjwyYpNPpvAFNr4+Di8kgSJCAe6uixvuGswAp9RPbzsZ6vk63NGVVXcdeddaPc24UDTzozjnYoDb+i+Gs8995wliTJL/3/m6/8tEGsaBUmCIMJVV11l+nVJNBrFN775TTS1dmHnJVdkHH/9m34P83NzuNfCsgL5cPfddyMQCODgOy9Juv2S39oJu8OGr/77V9d9LA58MlM8+OCD0IXAASIoRNgnBB5//HFMTk5acrxMGZ9kScZncQQ+l97s5+vG//HHH0coFMauPbXYtMWLX/3qV5YuHbn//vsxOjKC4NU3LitUE73kIFBRiS99+cvrDmw/+uijCAaDuOya31rT+y993S0gIjz44IPrmkexMJ5cDg4OFngmbKO78847MTvrwx9uCyatU/XGzgga3MC/fvELZdVArhAWa4Qt/zwV0E19YHvZZbHM/alQ9ufEkKphIRJNvJcVj2g0iqNHj6Fr625T9te1Jbafw4cPm7K/QjGuO8ul7hozj67rePvb377s3iSfWZgb6d/ka2fOoDH+99yoC7x25kxegoRGoFMTkWU/W2/1uVoI8+qa/vznP8fA4ADevvmmlAlHK72+6ypUOjz45je+Ydo8DEuv+/JxDfjlL38ZqqrGromEAIQ1CW0PPfQQRoaHceNb3pM229PQ3N6Nnfuvwve+933L4i1W6+3txQMPPIDt125GTWt10jHOCgf2v3U3Xnj+BTz99NPrOh4HPvOknJ/+Tk1N4fvf+x52A6iPfxBcSwRd03DnndYsR1wMfCb/4JHIvMBnoqt7ToHP2H/NDnwGAgE8+eSTuLptP7qr2/CLn//C8n9bqqriO9/5T9Q3uNHWUYF9lzZgfn7esoDf6OgovnnnnVB7tkLr3rJ8o82G4LW34Oxrr62rm53RDa+1czNaOzevaR+VVbXYtucgHv7JT0q+G20gEMDMzDQAcO1EVlBnzpzB/ffdh5vaw9hclfym0C4D79+2gAsX+02r+7NRJZqxrOoLK5YtyVyvxsZGNDc1YSac/TnRGLtv3z7T5sHMcebMGYTDoUTAcr2a27rhcLosLZGUD0YponytxmGlQ5Ik3H333cuCYVZ3/V5qYmIib8cqpPHxcfReuICe+Lmth4DJ6em8/E4apQvUeODT6lIGi/fCyxFi8TkzRCIR3HnHneiubssq29PgVBx4U8+1eOnQIdMfaC19YJCPhwcdHR2QJAmSJIGEDhK66f9v5+fnceedd6J7625s2pH9Nc+Nb34PVFXFHXfcYep88kHXdXz+nz8Pu8uGg+9K/2feffN21LRU41//9V/XVZKNA595Uq6BTyEEvvSlLyEaDuO3ljwFqiXCVSJWp82KTp2ZMj4B85obGUEtOYeHO0QEicj0FPwHH3wQgUAAN3QcxA3tl6O3rxfPPvusqcdY6Yc//CEuXuzH1dc2g4jQ1l6Brh4v7r77LtOfMOm6js985h8R0XSEbnxT0raE6vbd0Lq34N+/djv6+/vXdJyjR4+iv78fl159y7rme+DqWzA/N4cnnnhiXfsptKeffhqqqsHbWIHnX3g+r10SGTNEIhF89h8/gyqHwB9sTd/04bJGFVc2RXDXnXdwkKFEXLJvH3xRLevroZlQFHa7HVu2bMk8mOXV8ePHAQDtPdszjMyOJMto696GoyXc2T0QCCQeHL506KUNVVORZaeurg41NTWJn/OXEQicP38+8X05d5P/8Y9/DADYEw8H7gZBztPqLCMYpgl12c9WSXcuNevz5+GHH8bY+BjeteX1OWc53th5BWpcXnz9P75uahzEuLe+ePEiXnvtNdP2m8rv/M7vxAKfJAECgDD3wTAAfPe734XP58NNb/2DnP6ea+qbcOB1r8dPf/pT9PX1mTonq/34xz/GieMncMW7D8BZ4Ug7VpIlXPO+gxgbG8M3v/nNNR+TA58WM04u5Rr4/OlPf4rHH38cN2Ex29NwCxFqiPCpf/gH+P1+U4+7WDvV+uZGRhp9rh/4kmRu4HN0dBR33XkX9jZsxeaaTlzVtg9NFfX48pe+bFmg6vz587j99q+hZ5MXm7cu1sG86ZZ2RCJhfOYznzH14v7b3/42XnnlCAI3/BZEVXXyQUQIvuFtUGUZf/f3f7+mbMuf/OQncDhd2Ln/qnXNt3vLLtTWN+Phhx9e134KSdM03HPPPahq9OL6/3o1goEgfvSjHxV6WmwDuuOOO9DbdwF/tnMBnixKJ/3xjhBcso7/++lP57XOUzlJ/RDRvIeHhl27diEUVRHWstuvP6pi+/bteQ0OsOwcP34ctfXNqMixPnY67d3bcPHCBdOvF/Pl5z//OSKRWFfa6alpy2uhs9LU2NhYkOMu/fd46tSpgszBahMTE7jv+9/HbgB18Xs2DxEOCODhhx5ac7JEtoxgmIbosp+tkjh/r7g9JTInuB0KhfCtu7+FbbXd2FWf++o4u2zDWzbdgBMnT+D5559f93wMRjOf1tZW1NWtv8Z0to4cOQz/xDiELJu6onNiYgL33Xcfdh94HVo6NuX8/mtv+W3Y7A78x3/8h2lzstro6Ci++u9fRdvOZmy9uier9zRvacTOG7bi/vvvX/PqRA58WsxofFKqtRfSOXHiBP7lX/4Fm4lwfZIl5w4i/L4AJicm8Pd/93em1uHIWOOTJNOCzYml7jm+z8yMz3A4jP/zyU9CaDr+YNfbAACKJOOPdr0dIyMj+PznP296cH1mZgaf+MTHYbMT3vCWrmWB35paJ264uQ2HDh0y7YP20KFDuOOOOxHdsQfqrvQp78JTiYXfeid6z5/HF7/4xZyOEwgE8PjjT2Dn/qtgs6d/wpQJSRL2HLwOr7zyCoaHh9e1r0J54IEHcO7cORx4+x40b2lAx55W3HnXnWXXtIkVt6NHj+Kee+7BTW0RXNqQ3bmiyiHwZzvncfbcOcvKqpS7xEPEFedwM+tkG7Zt2wYA8EUynxeFEJiLqNixY4epc2DmOH36NFrWWCYmlbbOWGZvPjJ4zOb3+3HnnXeiaVMDrnz3AXgbKvHv//7vJV8Gh5nP4VjfdedaDA0N4emnn8JNbRG4bITvfe97eZ+D1TRNw2c/+1mo4TDeuCIp5hYi2ITAZyx+SGoEOvV4s8C8BT5XvE4g6Pr6k1J+/OMfY3pmGu/cevOaa1pe234A9e4a3PHNO0y7Tx0YGIAkEbxed97uVYQQqG9ogKerB5rTjQkT4zp33XUXVFXDDW/+/TW9313hxdU3vwNPP/10YjVGMRNC4HOf+xw0XcO177sip39bl//OflTUuPHZz352TedXDnxaKBgM4qknfwMAeO7ZZxJPKMpBf38/Pv6xj6FK1/FeUMr6lx1EeBeAlw8fxuc+9znTPvQWb9Yyj1mvWEHjNWR8EpkS7FVVFZ/+9Kdx8tQp/PGed6HBvbhMZntdD9659Wb88pe/xO23327anzkQCOCv//pjmJgYx9vf1QNPkvSrvfvrse9APe655x7cd9996zre6Ogo/u6Tn4SorUPo5rcmXeL+/7d333FSVecfxz/PFnrvHaSJqIgI9th77JrYorHFgsaaxJiYnyXNHkusUYkFE2Ni7w17o9kVFVRUBJHOUrY9vz/uDCzL7rLszLl3d+b7fr14sXNnd54zu3fud+bcc8+prmKDwazccnsef/xxHnnkkXrXeu2111i5cgWbbPGjTJq8yiZbbA/ACy+8kJXHi9PEiRO54YYb6DeiN4PG9Adg2yPH4Oacf/5vWbJkScItlHxQUlLCHy++iG6tnKM3XL/R62O6lbNT71LG33NPkGlVcl16xH71qycMy/rJtPQl63Wt7J62rLyC8spKXebeCM2fP58ffviBnn3qN0qjvnr0jR7vk08+yerjhlZeXs7FF1/EosWL2OaILSgoLGDbI0czc+ZMrrzyypy94kuahvLyci79618pNufQwSvYp99yXnzxRV566aWkm5Y17s6NN97IxIkT+TGrR3umtTXjQIePPvkkyECRtKqXuofu9ISqozrX/sxUWZnZc1y5ciX3jr+XDTttwNBOAxr8OEUFRew98Ed8Mu0TJk6cmFGbIPpbv/baq3Tv2Zo+/Vrz1ltvxnLFz8yZM1m5fDnevReVXbszY8aMrCwY9u233/L4448zcuud6dC54aPBR2+/J23atucfARaTyrYnn3ySiRMnMuagzWjbpc16/WyzFsVsd/SWzJw5k3/+85/rXVsdnwHddtttLF6ylOOGLWf58hXcdNNNSTcpK+bMmcNZZ55JRUkJxzi0Wkcn1SgrYBeMJ598khtuuCErgbP6MWqrnb0PbRUVFeu1sNHqFljGl4GXlpZy4YUX8tJLL3HYRnuzRY+1FxLYZ9AO7NB3NOPHj89K5+eyZcs499xzmTZtGvvsP4CevVvX+r077daXwUM7cN111zX48uiVK1dy/u9+R8nKlSzd9yewHpO9l269IxX9B3LV1VfX+9KdCRMm0LZ9R/oOGNqg9lbXoVNXevUbxAsTJmTl8eIyceJEzvvtebTv0Y6djt9mVcd+286t2eWk7fjyq684++yzWbhwYbINlZx33XXX8f3cuZy68VJaNOCq5qM3XE6Xls6f/3iJ5qddT+mMKmDNucgKrIDKLM9R2KpVK7p368bSsnV3fKY7RwcOXP/LviSs9FyB3Xr1z+rjtmzVhnYdOjepecrKysq4+OKLeeutt9n2iNF06dcJgD7De7L5vpvw5JNP8re//S2n51SUxquiooJLL72Uqe+8w7HDSujY3Nl/g5UMbB9NEfPuu+8m3cSsGDduHPfddx/bYIypZQq0TczYxaLPotdee22Qzs/ViwVWUlAQdn5PqDrtW7V2AJ7hMefZZ59l3vx57DNoh4weB2CbXiPp0KId48ePz/ixnn76aWbM+IJNR3Rm0826sHDholhGMN97771QWEj5wKGUDR1OydKlWenXGT9+PJix7a4HZvQ4zZq3YOud92PKlCmNehDAggULuP766+kxuCsb7TikQY/RZ3hPhmwzkHvvvXeNuYvrQx2fAbg799xzD/fddx979F3J7v1K2XfASh5++GFuvz17Q72TsGDBAs4680wWz5vHMb72vJ612cWMrYgur7jzzjuz2KKa62dztIq717p6fF3MMht1unTpUn79619HnZ7D9mK3AdvUUsc4auN92bHvGMaPH8+ll17a4JGmJSUlnHPO2Xz44Qfsvd8ABg3pUOf3FxQY++w/gA0Gteeqq67if//733rXvO666/js008p2eMAvON6ztVSUMCyvQ6ionUbfvf737No0aI6v720tJSJEycyePgoLItnY4duMppPp01rMlNaPPbYY/zqV7+ibdfW7HXWTjRruWZnc++NerDrydvz2fTPOPmUk4PPiyT5a+LEiTz++OP8uP8KhnZoWEdbyyI4efhSvv1udkaTnuej2qaNMQqpyMKlctVtMHAgJeXr/kCW7hzt3z+7nWuSuS+//BKALt17Z/2xu3Tv3WQWK1uwYAFnn3M2EyZMYKtDN2fYj9YcnTxq303ZdPdhPPDAA1zwhwsyWolWZH2VlJTwu/PP56mnnuLQQSvYoVc0Kq64AH41cimdildw9lln8fzzzyfc0oZzd26++WbuuOMORmHss47PpLtgbIvx3//+lyuvvDLrC5CtutSd8gZ8alx/tZ1QyXSOT3fnv/ffT592Pdioc+YnH4sLi9il35ZMnjw5o+P7pEmTuPzyy+nVpw0bbdKJfgPaMmTDjtz6j1t59tlnM25nbR577DEef/xxSkduibdpS2X3Xizt3JW77r6bt956q8GPu2DBAp544klGjNmRtu07rvsH1mHk1rvQslWbRj2VxS233ELJshK2O2pLrKDhr5KtDt2c4pbFXH311evV16KOzyxbvHgxF198MTfffDPb9CjlZxtGK9MeNmQFO/QqZdy4cVzw+983yVFUJSUlnHP22cz+9luOdui9HqMgzYx9zdgc47bbbuOBBx4I2NLVNbPB3eu+pr4ODQ2eOXPmMPbUsbwzdSrHjTiY3TbYts7vL7ACjtp4X/YdtCOPP/44v/71r9d7CP7SpUs566yz+Oijj9hn/wEMHVa/g3BhYQH7HbQBg4a0529/+xv33XdfvWu+8MILPPzww6wcvS0Vgxq4OmzLVpTscyg/zJ/PX//61zoPgB9++CHLly9n0LC65xBdX+nHy8ZlHCGVlpZyxRVXcOmll9Jjw278+Fe70qpdyxq/t/9mfdjn7F2Yv2geJ5x4Ai+//HLMrZVcV1ZWxlVXXkGP1nDIoMzmwtuoUwW79lnJ/f/5D59//nmWWpj70sfLgrUudc/+HJ8QdWSWlJWv841qSVk5XTp3plWrVllvg2Tm22+/pXmLlrTO4sJGaR279ODbb2c1+gECH3zwAcefcDzvf/A+Ox2/DZvuvtFa32NmbHnI5mz1k1G88vIrnPiLE5tMp640bR9//DHHH3csb7zxOscOW87B1fK1Q3Pn/0YvYUDrFVx44YVcffXVTW4+2oqKCq688kruuecexmAcZLVPu5ZmFnWO7oDx8MMPc/HFF2f1Mun0585Kr4h3xGdN92XwuNOmTePz6dPZqe+YrH2W3r7vFhQWFPL444+v989WVlby73//m3PPPZd27YvY78ANKCgwzIw99+lH795tVvW9ZPPvWV5ezu23386ll15KRf+BrNx251X3NT/6FNoOHMxvzjuPhx56qEGZ9dhjj1FeXsaYH+2VlfY2a96CkVvvwquvvsr333+flcfMpunTp/P4448zfOehdOyV2fuHFm2as8UBm/Luu+/yyiuv1Pvn1PGZJZWVlTz99NMcdeQRvPD8c/xk8ApO23Q5RanfcIHByRsv58ihy3nt1Zc56sgjeOyxx5rM5S/l5eX84YILmP755xyBMaABB8ICi4JpmBl/+9vfePXVV7PQspoPNJ7RIb/eZdapIYExffp0Tj7pJL77ZhZnbHE02/YeWe9aBwzdlZ9vciCTJ03mtLFj6z0CcdmyZZxzzjl8+uk09j1wA4ZsuH5nngoLC/jxAQMZsmEHrr/++nqN/FywYAGXX3kllT16U7rNTutVr7rK7j1ZsV10sH/mmWdq/b70ZT19B2Z3wYxuPfvRomWrRn15wezZszl17Kk8/PDDjNhjI/Y8fce1RnpW131QVw743Z606daK3/3ud9x4441ZXaRM8tuDDz7IN9/O4pihJTTLwmeEw4aspHUx3PD3v2f+YHli9YiXaosbmWU8R1hN+vbtS0VlJSvWsbL7svJK+mm0Z6P03Xff0aFT16x9IK6qY+duLFtW0mjnl66srOTuu+9m7NixrKxcwX6/3p3BW9U+16mZseluw9jrzJ35YcFcTvzFiTzyyCONvmNXwgk5HUtZWRm33347J598MisWfMcFo5eyR7+aV59u18y5YPRS9u6/kgceeIDjfv5zPvzww2Bty6aysjIuvugiHn74YXbEOKAenZ5pZsaeBQXsZcYLL7zAb887jxUrVmSlXek5PiupqM9SBY3Ws88+S1FBIWN6bpK1x2zbrDWbdh3Kc88+t159H9OnT+e008by97//nQED2/DTo4bQqsq6E8XNCjnop4PYZERn7rnnHo4//risfBb77LPPOPnUUxk3bhxlwzdj2X6HQWGVN6otWrD0kKMp7dOfK6+8knN/9av1WmjJ3XnyySfpO3BYVq+eGLnVzqv6pBqbO+64g+IWxYzcZ+1p+xpiw+0G0aF7O2677R/1zlR1fGbBe++9xyknn8Qf//hHOvkC/rTVEg4auJLqI3jNYN8Bpfx5qyV0L1zIpZdeyoknHM+UKVOSafh6uO2223h74kT2N2PDDI7mhWYchtEL45KLLuLrr79u0OOsfsNd247uWXtTXlBQ0KB+T/f1X9Hv448/5rSxp1GxrIzfbHUCw7us/6qp2/cdxS+3OIqvv/qasaeeyuzZs+v8/vLycv7whz/wyScfs8/+6768vTaFhcbe+60e+TlhHXNe3nzzzSwtKWH57vuuGSYNVDZySyp79uGa666rdbTrtGnT6NytFy1brd9kyutiBQX07DuIadOmZfVxs2XKlCkcf/zxfPHVF+x26o/Y8pDNKSis377ZplNr9v3Vbmy04xDuvfdezj77bBYvXhy4xZLrSktLGX/P3QzvVMHIeq7ivi5tip39Bixj4qRJfPDBB1l5zHyx9qrulv0TiECfPn0AWLaOeT6XV1Ss+l5pXOb+8ANt2mV+WV5N0o/bGKeNWbhwIeeeey633HIL/Tfvw4EX7EmX/p3q9bO9N+rBgRfsRdeBnbj88su5+OKLNR9xHlq6dCl//etfV92OVnPOTv7NmDGDk35xIuPGjWPb7iu4dOtFDOtY96XcRQVw9IYrOH+LpZTM+4ZTTzmFW265JZbFYhqqvLyciy68kBcmTGBvM/YoKGjQ570fWQEHmvH222/z2/POy8qI19UjPuOZ47MumZxcefWVVxnWeSCtimu+IqyhRnXfiHnz5/Hpp5+u83sXLVrE1VdfzbHHHcvn06exxz792e+ggTRvvvbvtaiogN337s/+Bw/kh3nfMXbsWC6++OIGjXpcsGABV1xxBccffwKffjWT5XsfxIo99oeiGiagb9GSZQccwYqd9mTilKkc9bOfceutt9ZrWpOZM2cyc+ZMho+seRq7hurYpTu9+w1udFfpfffdd7z8yssM22EwLVo3z8pjFhQWMGLPjZgx4wsmT55cv5/JSuVamNleZjbNzD43s9/WcL+Z2XWp+98zs1Eh25Ntc+fO5f/+7/8YO3Ys333xCSdvvIyLt1zCgHZ1n8no27aSC0cv5bRNlzHvm88544wz+N3556+zgyopH3zwAePHj2d0HZNGr49mZhwJeGkpf/nznxt0cF49gXTtIz6z1fFZWFhIZQPa6Piqs3/1MXPmTM45+2xaeDHnbXUifdp2X++aaZt0HcK5Y45l4Q8LOOvMs+rsqLrzzjt566232Hn3aKGiTBQWGvvsvwE9e7fhT3/6U61zQ3777bc8+eSTlG66BZUZrGK3hoIClu+4J0sWLap1KoUZM2bQpUeYD9Nde/blyy+/zPqcQZmaMGECZ59zNoWtjQPO34MBI/uu92MUFhey3ZFj2OHYrXnv/fc4+eSTmTt3boDWSkOtK28bm9dee4158xewb//sjLRI261PKS2Lo8vYpPHp3Tsa2bCsvPbjZFllJSvL1fHZWM2fPz/IZe4Ardt1AKIPn43JzJkzOfEXJzLlnSlsd9QYdvnFduu8YqK6Vu1bstcZO7PFASN4/vnnOeXUUxplB28uSvLzaEVFBe+//z7XXHMNBx9yCC+8MIGyvmMo7zKY22+/nZ/89DDGjRvHF1980eDOqscee4wTTjieOV/P4JyRJYzddDlVBsWt06adK7hs60Xs0Gsld999N6eeckqj/Ux63XXX8dLLL7OPGdtn+Jl0jBVwsBmTJk/m0nVMlVUf6cEu3ghGfDb0M/APP/zAt7O+ZXjn9R94sy7pwTx1DfiqrKzk4Ycf5vDDD+PBBx9kxGad+fmJG7Hxpp3X+ZwGDenAMScMY8ttejBhwvMcccQR3H333fU6uVBRUcH999/PTw8/nIcffZSVm41m8TGnUr7hOka9mlE2ckuW/PxUlg/ckLvuuovDDj+cZ555ps79KT012qCNRq6zbetr4EYj+fjjjxvVlRPPPfccXukNXtCoNgO3HEDzls3qvNqzqmAdn2ZWCNwA7A0MB44ws+HVvm1vYEjq30lAk1n2/LnnnuNnRx3Jqy9N4OCBK7hq20Xs2LtsrVGetTGD7XqWcdW2i/jp4BW89car/Oyoo3jiiSfCNrwBbrzhBtrauieNXh8dzNjLnfc/+KBBZyVWh0stHZ9eud6jLWtTVFSEu693IFa6U1xcv3ce5eXlXPD7C6DMOWfMsXRu2aEBLV3TBh368MstjuK7777jsksvq/F7Zs6cyZ133slGG3dixMguGdeE6MzbvgcMwAoqufrqq2v8nieeeIJKd0pH1z136fqq7NGLin4b8NDDa19KVllZyZw5c+jYueEdynXp0KkbpaWljeoD25QpU7jooovo0r8T+/5md9p3b5fR4w3dZiB7nbkzc+bO5uxzztZiDY1EPfO2UZkwYQLtmxsjumR36oQWRTCm60pefunFRncSojGqugptVU7DFvVbl27dulFYWMiystr/Nun70p2k0riUlJTQvGXrII/dokU0wmjp0qVBHr8hZs+ezWmnn8aipQv58bm7stEOQxrcqWAFxub7bMIep+/IzG9mcvovT9cVFIEl8Xl01qxZPProo1x44YX8eN/9OPXUU/nfAw+yuEV3Vow4iPI+m1M2eGdWDtuLOSsLuf322zn66KM55NBDufzyy3n++efrvRZEeg7CYe1WcNnWixjdrWGZ2qoYTtp4OWdtVsJXMz7lpF+cyFdffdWgxwrl9ddf54EHHmA7jO2yMBAHYJQVsJsZzz73XL07T2oT9xyfqz8Lr8mdel/6X116NOYGHbKfv+2bt6VTq/Z89tlnNd6/YMECzjnnbK644gradzB+dtwwdtmjHy1b1jDashbNmhWy3Q69OObEjejTryW33HILJ510ErNmzar1Z2bPns0pp57Ktddey9Iu3Sk56iRW7rQntKj/iFdv044Vex9EyU+PY15xCy655BJ+c955tR7fP/74Y9q260j7jtn57F1Vn/5R52Jjugrxtddeo+uAzrTtnN33DkXFhfQd0YtXX3u1Xv00IUd8bgl87u4z3L0U+DdwQLXvOQC4yyNvAh3MrGfANmXFQw89xEUXXUTv5ku5bJvFHDp4JS3q/5pcQ7NCOHDgSi7fZhEDWy/jL3/5y3otDhPa119/zXvvv8927jTP8umrURgdCgp47LHH1vtnV82j4jV/eHIq12u0ZV2aNWuWqrV+P1dZ6at+dl2eeuopZnwxg2M23p+urbJ3Cdngjv05YPDOvPTySzVe+vnAAw9QUAA/2jm7AdembTPGbNWNSZMm1TiZ/6uvvUZFr754m7ZZrQtQNmQ4c2Z/t2rl2bQlS5ZQXl5Om9SIkmxLr8g3f/78II+/vsrKyvjTn/9E265t2POXO9G81fqNTqlNz6Hd2PWUH/HVl1/yz3/+MyuPKRmrT942Kh+8/y4bdVx7Sphs2LhTOSXLlvPFF19k/8FzTG0nEd2zl6FVFRYW0qNHD5bVMQIjfV+vXr2yXl8yt2L5cpo1y86latUVN28B0KhOqv3lL3+hZHkJ+5yzC902yM6H1L6b9GLP03dk1qxZXHPNNVl5TKlVLJ9HZ8yYwd///nd+ethh/PSnP+Wyyy7jhVffZGGL7pQO2YVlWxxN2dDd8NapfciMyo79WDl8X5aPOorSDbbnu9IWPPrEU1x44YXst99+HH/CCdx11121jgx+4YUXGDduHDv2KuW8USW0b5759CRbdi/n4jGLqVi2iPN+8+tGtejRP269la4FBeyR5c+jO2L0MeMft9yS0dobq1d1r4xlVfd0Rlfv73GgoIH5nZ6nsnur7HfIAXRv2Zlvv1l7LsylS5dy2mljeeedqey6Zz8OPWIwXbo2/FL79u2bs//BA9n3wA2YOXMGp5xySo2Xvs+ePZtfnHQSn0yfwfK9DmTZgUdS2blrg+tW9upDyWHHsWKHPXjzrbc4dezYGqdf+/qbb+jUrVeQubI7d4/eO33zzTdZf+yGqKio4NPPPqX7oED71KCuLF60mDlz5qzzexvYXVcvvYGqEzh+A2xVj+/pDXy3rge/9tpraz1jkDZv3jz+/e9/r7W9devWHHvssesqwZAhQzjzzDPX2FZSUsKVV14JwLAOZbw8a+0Rfbc8+ymfzY6GF7coLmBF2eqD6Ak7D2KjPh1qrDe4fRkfzS/k+uuvZ88996RDh9XfV5/nu3TpUu68806KioqorKyksjL64GJmHH300etcHbWm5/vRRx8BMNOd57zmMHj/+zk8XKVtRnTQHdCuHT/bdESdNRe68+7UqXV+T02Kior47rvvuHfOxaxYsYKWzdvQq+MGDOg6jKfeHU+PQe2z9qGtRYvojfhnC5esOoP21BtvU1xURNtWLfl27jx6d+uCAWOGr140p7yyctXPrkt6oacvF83iq0U1n5W68F9XUlq29iTlHVq157xDT6v1sVdUlK6qsckmaw7ZnzjxbcrLK3l3Ss2XLZeXV3D9NQ8Bawdr7z6dOeyInWqtu2RxVHfq1KkMHDhw1XZ3jw7G7TrQ7I0Xa/35SVdfWuP2dgMHMfTAn9T6cwWLFwFReG+wwepFB9LzaX356QcsX1bzaJL/3vMPZnxe8xmyfgOHcPgxJ9dad+EPc9aok1af1+706dN56qmnarxvv/32o1+/fnX+fE2v3alTp/L9nO/p1KcD7z/7cY0/9+T4Z5j+4ZqdQ2ZG5+6dOPTUAykqrj0i3OF/D/yPsWPH1tk2iUV98nYt65ujhYWFqz4UmBkjR45km23qnp+opn2zoqKCuT/Mp0P7Au7/vPYOlN/cM5lKjzKlwKAidQxq26KIC3+yWe1tXhF9+Pj+++8ZPHjwqu31eb4ffvghL774Is2bN2flypV07dqVuXPnUlRUxLHHHkvz5nV3+NT0fOtTF6LF12pa9O9nP/sZ7dvXfWlxTXXro6ioiNLSUm6adDo7b3QoT75zNyvKljNs8HCK2ocZsdK3b1/e/PZbPl0QvU/697MvsPc2W1JYWMhnM7+hS6/oEvf1HfFZn9/zTTfdVOsH29NOqz1H06r/nutTs7Kykptvvhn3aAqeqqMSzGydx9CG7lPuzo033ljjfS1btuT4449f77qVlZW4OzNnfMJLT91f689eeclvavw977LXAYze+ke1/tzK5ctW1amqPs/37bffXnX5YHWbbropO+ywQ50/X9Pz/eabb5gyZQpFzQqZMWkmsPb0Pa8+/jrvvvEBnjpYGUZhYQHl5RXs9pOdGbb50FprVlZU8swzz3DuuefSunWYUbQS9vMowL/+9S9uuOGGVbe9sBmV7XtT2bIDYNiyhUy96y945ZqDNYpbtaFLvw3pOyK6+qmyVWdKW3WkYOlcChd9y6fTpvHptGnceuutXH311Wy55ZZr/Pzdd90JQPvmlfxv+trZtHhZGZf8b82FXooLjaICY+O+HTh8u9oX5urcvIwZ387ilVdeYbfddlu1vT6vxVmzZvHggw8CUWdgYWEhzZo1o7S0lB122IFhw+peYLSm1+L8+fP57PPPKQZexNb+UAI8M306b89e/SdLfx4FOGfLrWhVx1V4i3EWf/89X3/9Nf0buLBe1fUnCgoLOfroo7n77rsb9Fj1UVhYyPLly3nu3Vls0LM7c+bNp3e3rkyY+h4HHNiwAS3pEYrPffUGBbV0377xyWQeeXvtxXPatWzD+T85o87H/3jeDHoVr31O4b///S8zZ35N7z5tWLqklDdeXfuld9PfH2X58rU/BzdrVsTpZ9Z+rr9Pv9Z8/ul8xo0bx3nnnbfGfXfccQcL5s+nbNCGFCyYR7M3X1rjfq+sZPK1V9S4vxW2bMnmp9b+vqu8W0+++vJL7r///rX6nRYtXMjKcmrN0RXLl3PdZX+o8b527Ttyytm/r7Vu+jhTfdR4fV67s2fPrnWB4gMOOGCdUxDV9NpdsmQJpStLWTBrEZMfqXnhqQU/LGT81asH/jVr3oyysjK80hm142Zsu+fWtdZc9H30XnLu3Ln06NGjzvaF7Pis6dVSfa+pz/dgZicRXXqwzg6Aqjp06MCQIUMoKSmhb9++zJ49m+Li4jU6YdZXeXk57du1Y9HixTz6Zc1nIlp224AOK2ZSWVlJly5dWLZsGSUlJbRs2ZIPl3dl2hd1/dqdtm3aNGiy69atW7PhhhvSo0cPVq5cSWlpKc2bN6e4uHidnZ616dixI2bGh+58VMtJifLOnem9bBmLFy+mbdu2FBUVMX/+fNoNGMCL6zqR4dC7AfN49ejRg969e7N8+RKKKiqoLCxjdtESfljyEa17QoWXZfR3rmrgwIEUFxfz5ZLVHVrd+/SlsLCQwsJC+rVpR4sWLXB3ZixePUqhqLCQQYPqN0dKp07RBPlPzKj9sv8+ffuwePFili9fTtu2bVm6dCmtW7emY8eOPF7Hz6VV7UhP69atG199NZO336j9LEmfPn2prKxk5cqVlJeXU1RUxPLly+nUsVedP5f+cFdb3W++/pqiubX/fJcuXSgoKKC0tJSlS5fi7rRp04YBbdvQ4u21OweqKmrWjLZt1xxN2qZNGzp07Mj0T95h+ifv1Phz7dq0pE2bNjRv3pwlS5ZQVFTEihUrMDM6tm3F6889VGfdVq1arfpbro++ffsycuRIvv/+eyoqKujVqxeLFi2iefPmDb7cs3nz5hQUFDD/m4Us+HZRjd/Tpqgdbdu2pV276BL4efPm0aVLF9q3b88Hz677Eonu3buv+jAviVpnljY0Rzt16rTq8uQhQ4awePHiVVN4rOvDS23cnZ49uvPZ7Dl8tqj2DyI9e/WmpKSEFi1aRJdHL1tGWVkZ3Xv04KEv6h4J0LxZcY3HnnUZOHAg33zzDQMGDGDRokX07NmT6dOn06lTp3V2emZq6NChfP3115SVldGmTRvmzZtHhw4d1tnpmYm+ffvSpk1bFlcuYtKSB2jRrZLm3oylhXMYNSTMtOujRo3izTffXJWX/QdswA8VBhWVNO/YmdLKSjYaNqzB71vqsvnmm1NcXLxqSpL0e6WOHcMs1gPRB//Ro0cza9YsevbsSVlZGcuWLaOiooKuXRs+smRdzGzVe5Dly5dTXl5OWVkZhYWFjBw5ssGP269fP77+YhrffFF7RvTr1485c+asGgFVVFQU5eq8WevM0WbNmtGz5/pf/LXxxhuzaNEili1bRufOnamsrGTevHm0b9+eESPqPglfGzOjVetWLCtZxjtP1LzideGKZvTs0ZOlS5fSpk2bVe8N582bx9Kvl/POd3WvlN2hY4cGtU3qLfjn0aqXhJsZVllGwYIvocrMR/369qG0tJRFixatOrZ17tyZNq2h2awaBoCkpyFJvZeu6VLd9Pvc2j6Purege/fuLFu2bNVJi65du0YDZNq3rzNH03Xre+VaVd26dVs10KJ58+arPhuWlZU1uFOxqKho1efv2j5bVvTuRceVK2jRogXFxcW4O8uWLaOoqIi3iovqnHfTHVq3apVRzrdu3ZpuXbsz94fvGThoIL/85ekNfqz66NatG126dGF+QQELvABr25655U6//v0ZPrxhsx2lr7R4csYrtX5PmZXRoUMHWrRoQWlp6aqBRh06dKjX59F+A9beB4pSiwfN+raEWd/WvEBtnz79V2Vn+m9bXFxMly5d6vw8mlbT9HPp/bvZjE9hRs2LLvXr25dFixZRXFxMZWUlK1asoLCwkO7du6/z86jXUrd3nz5MfPvtOvOwY8eOq1676X6hDh060K1bt3XmaEFBQYOumunatSuDBw+mZcuWlJeXU1FRwcKFC2nfvn2Dr8Jp1qwZnTp1YtYnc5j1Sc1/p8rKylXvs0tKSujatSvl5eXMmzePgpLiWvM3rWWrlqs+y9bFMp3It9YHNtsGuMjd90zdPh/A3f9a5XtuAV5093+lbk8DdnL3Ws+wjR492idNmhSkzSIi0jBmNtndRyfdjnxUn7ytSjkqItL4KEezT59HRUTyR105GnKOz4nAEDPbwMyaAYcDj1T7nkeAY1Kr6W0NLKorZERERGQt9clbERGRfKPPoyIiEu5Sd3cvN7PTgaeBQuAOd//QzE5J3X8z8ASwD/A5sAw4LlR7REREclFteZtws0RERBKlz6MiIgJh5/jE3Z8gCpOq226u8rUD655NXkRERGpVU96KiIjkO30eFRGRkJe6i4iIiIiIiIiIiCRCHZ8iIiIiIiIiIiKSc9TxKSIiIiIiIiIiIjlHHZ8iIiIiIiIiIiKScyyaz7npMLO5wFcN/PEuwA9ZbI7qqm5SdfPpuapu06jb3927ZrMxEoZyVHVVN9G6+fRcVXf9KEebCOWo6qpuXtbNp+faVOvWmqNNruMzE2Y2yd1Hq67qNvW6+fRcVTf360rTkW/7puqqbi7UVN3crytNR77tm6qrurlSN5+eay7W1aXuIiIiIiIiIiIiknPU8SkiIiIiIiIiIiI5J986Pm9VXdXNkbr59FxVN/frStORb/um6qpuLtRU3dyvK01Hvu2bqqu6uVI3n55rztXNqzk+RUREREREREREJD/k24hPERERERERERERyQPq+BQREREREREREZGco47PAMystZkVpL4eamb7m1lxzG3oaGYjYqqV+PPNJ2ZWYGbtYqhTaGZnh65Tj3bEti83Bvn2fEVq0hhyRTma2+LIUuVoMvLt+YrUpDHkinI0t+XTZ9J8y5VcfL552fFpZm0Cl3gZaGFmvYHngeOAfwauiZm9aGbtzKwT8C4wzsyuDl2XhJ5vdTF9iBlsZtvVsP1HZjYoYN17U3/b1sBHwDQz+3WoegDuXgEcELJGbRLclxORxPNNal+W3KAczTrlaAzHnrizVDkaH+WoNDXK0axTjuZgjkJyWaocza0czcuOT6IXaUjm7suAg4Hr3f0gYHjgmgDt3X1xqu44d98C2C2Gukk93yQOvtcAS2rYvjx1XyjDU3/bA4EngH7A0QHrpb1mZn9PHXxGpf/FUDf2fdnMrjez62r7F7I2ybx2ryGZfVlyg3I0u5Sj8Rx7kshS5ahyVKQmytHsUo7mbo5CMlmqHM2hHC3K5oM1JmZ2Tm13AaHPsJmZbQMcBZyQ2hbH77rIzHoCPwV+H0O9tKSeL6QOvmZ2FNHB9zxgMnBFoHoD3P296hvdfZKZDQhUE6DYoss1DgT+7u5lZuYB66Vtm/r/kirbHNglcN0k9uVJqf+3I3qjdF/q9k+I9qmQkni+Se3L0kQoR5WjgeoleexJIkuVo8pRyVPKUeVooHr5lqOQTJYqR8OLbV/O2Y5P4C9EB5vyGu4LPdL1LOB84EF3/9DMBgITAteE6EDwNPCqu09M1f0shrpnkczzhfgPvi3quK9lwLq3AF8SDTt/2cz6A4sD1gPA3XcOXaMWse/L7n4ngJkdC+zs7mWp2zcDz4SsTTKv3aT2ZWk6lKPK0RCSPPbEnqXKUeWo5DXlqHI0hLzKUUgsS5WjOZSj5h5HB338zOx14JfuvlbPuJl97e59E2iWZJmZnUF0Vu1d4MdEw+3vcfcfBar3L+AFd/9Hte0nAHu4+2Eh6tbSliJ3r+mNVDZrtAcuBHZIbXoJuMTdF4WsmyQzmwZs4+7zU7c7Am+6+4bJtiy7GtO+LI2TcjQ/5HOOpuoGzVLlqHJU8pdyND8oR/WZNNuUo9nfl3O543NDYJ67/1DDfd3dfU6Amo8SDbmukbvvn+2aqbrXr6PuGYHqJvJ81yXkwdfMugMPAqWsHm4+GmgGHOTus7Ncr7ZLZABw99ATDv8P+AC4M7XpaGAzdz84UL1E9uVqbTgOuIjVZ4l3BC5Kn4HLcq3Enm/c+7I0PcrRNeoqR7P32LEfe5LMUuUooByVPKUcXaOucjR7j51XOZqqH1uWKkfXlCs5msuXun8FtK2+0cy6AaHODFwZ6HHXZdK6vyWIpJ7vKqkXy1+AXu6+t5kNB7YBbg9RL/UGZVsz2xnYJLX5cXd/IUQ9atiHYzbI3Q+pcvtiM3snYL2k9uVV3H2cmT0JbJXa9NuAH14Se74J7MvS9ChHw1OORkIfe5LMUuWoclTyl3I0POVoJJdzFOLNUuVoTOLcl3N5xOetwFPu/kC17UcB27v7qcm0TLIpdUAYB/ze3TczsyJgqrtvGrjupsCw1M2P3f2DkPWSYmZvAL9291dTt7cDrnT3bZJtWThmZkQTow9090vMrB/Qw93fTrhpQeTLvizrTzmaH5SjYSlHlaOSv5Sj+UE5Gl6+ZalyNPv7ci53fH7k7sNrue9Dd984QM33qXmYsAHu7iOyXTNVN6lLGhJ5vtXaMNHdx5jZVHffPLXtHXcfGahee+BhoC/wHtFz3RSYCRzg7lmd3NnMrqvr/tBD7c1sM+AuoH1q0wLg517D6mtZqpf45SpmdhNQCezi7hul5lR5xt3HBKiV2PONe1+Wpkc5uppyNKv1Yj/2JJmlylHlqOQv5ehqytGs1surHE3Vjy1LlaNrypUczeVL3a2O+0KtordvoMddl6SG+Cf1fKsqMbPOpF6sZrY14S4dAfgj0XDwXdy9MlWzALgU+DPwyyzXW2sy9JgtTp25bAfg7ovNbIOA9RK/XAXYyt1HmdlUAHdfYGbNAtVK8vnGvS9L06McDU85SizHniSzVDmqHJX8pRwNTzlKzucoxJulytH4xLYv53LH5/dmtmX14cBmNgaYG6Kgu39VpU5/YIi7P2dmLQn4u3b3l6rUbQn0c/dpoepVqZvI863mHOARYJCZvQZ0BQ4NWG83YET6hQng7pVm9jvg/WwX82oTGJtZa3cvyXadOvwPGFXtbMt/gS1CFEtqX66mzMwKWf3mpSvRGbesS/j5xrovS5OkHA1MORoJfexJOEuVo8pRyV/K0cCUo5Ecz1GIMUuVo7mZo6HONDUGvwb+Y2YXmdl+qX8XA/9J3ReMmf2C6IV4S2pTH+ChkDVTdfcD3gGeSt0eaWaPxFA3kecL4O5TiFY52xY4Gdg41OVjKaVewwp9qW0rQxU1s23M7CPg49TtzczsxoD1hpnZIUB7Mzu4yr9jgRah6lapn8i+nHId0epy3czsz8CrRBOWB5PQ801kX5YmRTmqHA0hsWNPnFmqHFWOBq4tTYNyVDkaQl7kaOrxE8tS5Whu5WjOjvh097fNbCtgLHBsavOHRMOGvw9c/jRgS+CtVFs+s2j1vtAuStV9MVX3HTMbEEPdpJ4vZnZwtU1DzWwR8H6gv3MLM9uctS9dMaB5gHpp1wB7Ep1NxN3fNbMdAtbbkOjSkQ7AflW2LwF+EbBu2kUksy/j7uPNbDKwK9Hf9UB3/zhw2YuI//kmtS9LE6EcVY7mWI5CvFmqHFWOSp5TjipHlaMZSzJLL0I5OiBwzdj25Zzt+ARw9znAhamhwbh7kEsKarDS3UvNor+fRSu7xbGKVLm7L0rXjVFSzxfgBGAbYELq9k7Am0SBc4m7353let8BV9dy3+ws11qDu39d7W9bEbDWw8DDZraNu78Rqk4dktqX0z4DFpM6RppZP3efGbBeEs+36r5c9fVqBN6XpelQjsZGORoJfuyJK0uVo8rROBsijZdyNDbK0UjO5GiqVpJZqhwNL7YczdmOT4v+YhcSnf0pSG2qAK5390sCl3/JonkJWprZ7kRn+R4NXBPgAzM7Eig0syHAGcDrMdRN6vlCNNfFRqk3FZhZd+AmYCvgZSCrQePuO2fz8dbD12a2LeAWTWx8BqlLDEIws9+4++XAkWZ2RPX7PfDKfSS3L2NmvyQ6dswhCnIjOhCHXBUy9ueb3pctmstlLLA90fN8heg1JHlOOaocJbdyFGLMUuWocjRkbWkalKPKUZSjGUk4S5WjOZSj5h7XiZB4mdnZwD7ASe7+RWrbQKJf4FPu/reAtQuIzvzsQbSTPg3c5oF/2WbWCvh9qi6pun9y9xWB6ybyfFO133f3TavcNqLLCjYxs6nuvnmgui1Y+8V5c6jftZl1Aa4lmgDYgGeAM919XqB6+7n7o2b285ru92oTXAeon8i+nKr9OdElSEF+t7XUTPL5/ofobOL41KYjgA7u/tPQtaVxU44CytGcydFUzdiyVDmqHFWOinIUUI4qRzOrl1iWKkdzK0dzueNzKrC7u/9QbXtX4JlQB6BUjdbACnevSN0uBJq7+7JQNZOU5PO1aDLlfsD9qU2HAN8QTRj+WKgzYqkX5xLgntSmI4CO7v6TEPUkPmY2gejYsdZEy7nIzN51983WtU3yj3I0PspRQDmaM5SjylGJKEfjoxwFlKM5Qzma/RzN5VXdi6uHDKyaV6U4cO3ngZZVbrcEngtcEzN71sw6VLnd0cyeDl2XhJ5vymnAP4GRwObAXcBp7l4S+DKADd39BHefkPp3EjA0VDEzu7OGv+0doepVqTPUzG41s2fM7IX0vxjqJrUvA8wAXjSz883snPS/kAUTfr5TzWzrKrW3Al6LqbY0bspR5WjO5Cgkk6XKUeWo5DXlqHJUOZqdurFnqXI0t3I0Z+f4BEobeF82tHD3pekb7r7UoqHDoXVx94VV6i6weFazS+r5krp84b+pf3GaamZbu/ubEMub3BE1/G2DnSWu4n7gZuA2Ak5cXYOk9mWAmal/zVL/4pDk890KOMbM0pNl9wM+NrP3o6Z4yLlkpHFTjipHQ4o7RyGZLFWOxkM5Ko2RclQ5GlK+5Cgkk6XK0RzK0Vzu+NzMzBbXsN2AFoFrl5jZKHefAmBmWwDLA9cEqLQqq32ZWX/iWc0uqedL6szA9cBGRAeFQqDE3dsFLh33m9wCM+vo7gsAzKwT8bx+y909iQn6k9qXcfeL46hTTWLPF9grpjrS9ChHlaMhJdFZlESWKkfjoRyVxkg5qhwNKV9yFJLJUuVoDuVoznZ8unthguXPAu43s1mp2z2Bw2Ko+3vgVTN7KXV7B+CkGOqeRTLPF+DvwOFEZ4FGA8cAg2OoG/eb3KuA183sv0QHoJ8Cf46h7qNmNhZ4EFiZ3uju8wPXTWpfxsweZe2D/CJgEnCLh5ngObHn6+5fxVFHmh7lKKAcDSmJzqIkslQ5GlGOSt5RjgLK0ZDyJUchmSxVjuZQjubs4kZJM7NiYEOiM3qfuHtZTHW7AFun6r5R07wygeom9XwnuftoM3svfVbLzF53923jqB8nMxsO7EL0O37e3T+KoeYXNWx2dx8YQ+2k9uVrga7Av1KbDgNmE80V1M7djw5UN5HnK9JYKUeVoyHEnaXKUUA5KpII5ahyNIR8+kyqHM2dHFXHpzRpZvYysBvRfB+zge+AYz3QSpoWrbBWnxfNP939rhBtkLDM7GV336GmbWb2obtvnFTbRESyTTkq2aYcFZF8ohyVbFOOZl/OXuoueeNooAA4HTgb6AscErDesfX8voUB2xCb1KTg5wD93P0kMxtCtILgYwk3LaSu1eY36Qd0Sd0XeiJ6EZG4KUcDUo4qR0Uk5ylHA8vDLFWOZplGfEpGzMyAPu7+ddJtSYqZFQBt3L2mycubNDO7D5gMHOPum5hZS6Jh7yOTbVk4ZrYP0aqB04mG+W8AjAVeBH7h7tdksVaRu5dn6/FEpOlRjipHc41yVETipBzN7RyF/MtS5Wj2qeMzi8xsVF33p1eZi6EdrYDhwFfuPjeGepPdfYvQdWqpvR1wEdCfKiOYY5jv417gFKCC6CDcHrja3a8IVO8ydz9vXdsC1E3PWTPV3TdPbXs31KUbNdTvRpVVL9NnvWKo2xwYxuo5gkJMII2ZTXH3Oo8bIvlEORq/fMnRVM3Ys1Q5qhwViZNyNH7K0dz+TKoczQ261D27rqrjPieaBDjrzGx/4DpgPnABcAMwBxhgZue5+50h6lbxppmNcfeJgevU5HaiSwomEx304zLc3Reb2VHAE8B5qTaECprdUzWq2ruGbdlWmjqj5gBmNogqK+mFktqnrwJ6Ad8TvZH4GAg2n4mZ7eLuL5jZwdXuGmhmuPsDIcoGeEyRpkw5Gr98yVFIJkuVo8pRkTgpR+OnHM3Bz6TK0dyijs8scvedEyr9R2APorM8E4AR7j4jdXbieSB00OwMnGJmXwIlRC8eT69qF9gid38yhjrVFadWDjwQ+Lu7l5lZ1odPm9mpRMPaB5rZe1Xuagu8lu16NbgQeAroa2bjge2o/7wymfgj0Ypyz7n75ma2M3BE4Jo7Ai8A+9VwnwMhgqarmZ1T253ufnWAmiKNlnJUORqiUMJZqhyNKEdFYqAcVY6GKJSnn0mVo+ROjqrjMxAz24RoeH/VYdGhVlWrdPdPU3W/cPcZqXrfm1kc8zXsHUONNVS5jGOCmV1BdBBYddYnhss4bgG+BN4FXjaz/kCIOVXuBZ4E/gr8tsr2Je4+P0C9Nbj7s2Y2heigb8CZ7v5D6LpAmbvPM7MCMytw9wlmdlnIgu5+Yer/40LWqaYQaEOenGkTWR/K0bDyKEchwSxVjganHBWphXI0LOUokNufSZWjOURzfAZgZhcCOxEFzRNEB+JX3f3QQPXeTdUrIDpDsBOrd9wJMc19sT0wxN3HmVlXosmVvwhYb0Idd7u7B7mMoy4WcGLg1HD+b9x9pZntBIwA7nL3hSHqValb03wfi4jm6wn2JsbMniM6e/lXohXsvgfGuPu2oWpWqX0mMA5YAvwDGAX81t2fCVArL+ZUEVlfylHlaKDHjz1LlaPKUZEkKEeVo4EeP28+kypHc4s6PgMws/eBzYCp7r6ZmXUHbnP3moYsZ6Pel0AlNffSu4efWPlCYDSwobsPNbNewP3uvl3IuklK/U3/AvRy973NbDiwjbvfHqjeO0S/4wHA08AjRL/vfULUq1L3TaID7XtE+9cmqa87A6eEOPim6rYGVqRqHkV02cx4d58Xol612u+mXrd7AqcBfwDGhQgEqzJBt4isphxVjgaq+Q4xZ6lyVDkqkgTlqHI0UM13yJPPpMrR3FKQdANy1HJ3rwTKzawd0dmBYAd7dx/g7gPdfYMa/gUNmZSDgP2J5lPB3WcRzfcRnJl1NrPrzGyKmU02s2vNrHMMpf9JdLDvlbr9KXBWwHqVqbNZBwPXuPvZQM+A9dK+BDZ399EerZS4OfABsBtweaii7l7i7hVAK+BR4B5Sk1nHIP2GbR+igHmXmt/EZcOugR5XpKlTjipHQ0giS79EOaocFYmfclQ5GkLefCZVjuYWdXyGMcnMOhANS54MTAHeTrRFYZV6NHQ4vcpa6xhr/xuYCxwCHJr6+r4Y6nZx9/8QndkkFQAhV/ErM7MjgGOAx1LbigPWSxvm7h+mb7j7R0ShMyNkUTM72czmEJ3Jm0T0OpoUsmYVk83sGaKgedrM2pL6O2ebxzAnjkgTpRyNT77kKCSTpcpR5ahIEpSj8VGOhhd7lipHc4sWNwrA3cemvrzZzJ4C2rn7e3X9TBP3HzO7BehgZr8AjicK2Th0cvc/Vrn9JzM7MIa6Jakzeelw3ZponpFQjgNOAf7s7l+Y2QZEZ51Cm2ZmNxEFOsBhwKdm1hwoC1j3V8DGHs8CENWdAIwEZrj7MjPrRPT7F5GYKEeVo4EkkaXKUeWoSOyUo8rRQPLpM6lyNIdojs8sMrNh7v5JLZPvxrGyW6zMrLm7r0x9vTuwB9EQ7Kfd/dmY2nAl0ZmX/6Q2HUp0gLowcN1RwPVE84t8AHQFDs21NxRm1hIYC2xP9Ld9FbiRaL6TVu6+NFDdp4CD3X1ZiMdfR+3tgHfcvcTMfkY0n8y17v5V3G0RyTfKUeWocjRrdZWjInlIOaoczbUchWSyVDmaW9TxmUVm9g93/4XVvMKbe8CV3cysAHjP3TcJVaOGmlPcfZSZ3e3uR8dVt1oblgCtWT30u4DU3C5Ev/N2AWsXARsSHXynuXuwkRtmNoRoRbnhQIv09jjmzDGzZkTP0wn8PKvU3JxoJbu3gJXp7e5+Rgy13yOaDH4EcDdwO1Ho7Riw5tZEb1w2ApoBhUBJyP1XpDFSjsYvX3I0VS+RLFWOKkdF4qIcjZ9yNDc/kypHcytHdal7Frn7L1L/75xA7Uoze9fM+rn7zJjKNjOznwPbmtnBNbTpgdANcPdYJq2urobnO9TMFgHvu/v3AUqOAy4E/gbsTDTUPdQEx6uY2U7AnUQTShvQ18x+7u4vBy59C/AC8D6B5jOpQ7m7u5kdQHRm7fbUfh7S34HDgfuJVko8BhgcuKZIo6McXatNytHsij1LlaPKUZE4KUfXapNyNLvy6TOpcjSHclQdn1lU08G2qhgOvD2BD83sbVafZcLd9w9U7xTgKKADsF+1+xwIHjQAZrY/sEPq5ovu/lhd358lJwDbAOmzqTsBbxIFziXufneW67V09+fNzFJD3C8ys1eIgiekq4A93H0agJkNBf4FbBG4brm7nxO4Rm2WmNn5wM+AHcyskBgm7Xb3z82s0KPVA8eZ2euha4o0NsrRNShHsy+JLFWOKkdFYqMcXYNyNPvy6TOpcjSHclQdn9lV/WBbVRwH3osDP351Pd39VDOb6u63xlwbADO7FBgDjE9tOtPMtnf33wYuXQls5O5zUu3oDtwEbAW8TDQkPZtWpC4f+czMTge+BbpluUZNitMBA+Dun5pZHCv3TTCzk4BHWfPSgjhWnTsMOBI4wd1nm1k/4IrANZelLt94x8wuB74jumRGJN8oR2OWRzkKyWSpclQ5KhIn5WjMlKM5+5lUOZpDOao5PqXBqsypMsXda5xAO4Y2vAeMdPfK1O1CYKq7jwhc931337TKbSO6rGCTVPBunuV6Y4CPic5m/hFoD1zu7m9ms04NdccRhWo6OI8Citw96KpyZvZFDZs9jvljkmBm/YE5RPOpnE30973B3acn2jARCUo5Gl+OpmrEnqXK0XgoR0Xyk3I093M0VTf2LFWO5laOquMzgNTZlr8Avdx9bzMbDmzj7rcHrhvrhLRm9izRqOGRwCvV7w94SUPVNrwH7JQ+82JmnYguLwgdNDcC/YjmwAA4BPgG+DXwWBLz6oRgZs2B01i9gt7LwI2eWj0xF9XyOlrq7u0D1jzT3a9d1zaRfKEcjShHmz7lqHJUJAnK0YhyNDfkW5YqR7NPHZ8BmNmTRBP//t7dN7NotbWpVc/IBKo7ibUnpB3i7r8LVK8ZMIrozMuJ1e9395dC1K3WhsOBy4jmNjGiuVXOd/d/B65rROGyXaruq8D/PMsvKDN7pK77Q4a5JbAyY5XaPwGecvclZnYB0X72R3efGkPtWF9HqZprnaUOdaZWpClQjkaUo1mrlUiWKkeVoyJJUY5GlKNZq5V3n0mVo7mVo5rjM4wu7v4fiyakxd3LzawijsIe44S07l4KvGlm27r7XDNrF232JaFqVpU6CFYCWxPNq2LAee4+O3TtVKD8N/UvpG2Ar4kmb36LGFbNS/NkVmZM+4O7329m2wN7AlcCNxPNWRNcXK8jMzuCaP6WDaq9oWgLzAtRU6SJUI7GIE9yFBLKUuWoclQkQcrRGChHw0swS5WjOZSj6vgMo8TMOhNNIJ0eqrwohrpJTUjb38xeIHpxmJktBI5398khi6YOgqe7+3+AOs9CZVuMl3H0AHYH0gekx4F/ufuHWa5Tm7hXZkxLvzH7MXCTuz9sZhcFrpkW5+vo9dTjdyFarTBtCfBeoJoiTYFyVDmaTUlmqXJUOSqSBOWocjSb8vEzqXI0h3JUl7oHYGajiA5CmwAfAF2BQ9096I5j0YS03wPFrJ6Q9kZ3/zxw3feA09z9ldTt7VN1g85rkqr1B2A5cB9rHgSDrrZWy/Dzwe7++4A1mxOFzRXAJe5+fahaVWruWNP20JeNmNljRKsE7gZsQfQ3ftvdNwtZN1W7pomdg7+ORGQ15ahyNGDdWLNUOaocFUmCclQ5GrBuXnwmVY7mFnV8BmLRPCobEg0Dn+buZQk3KRgze83dt1vXtkC1E1ltzcwmuftoM3svHahm9rq7bxugVnOiM01HAAOIzibe4e7fZrtWLfV7AFsSnTGeGMelG2bWCtiLaGXCz8ysJ7Cpuz8TunaczGwJqTPx1e8i2o+DTAQv0hQoR5WjWa6XWJYqR8NRjorUTjmqHM1yvbz6TKocza0cVcdnIGa2LdEBYdV0Au5+V6Ba71PzzpquG3pFub8BrYjm/HDgMGAB8L9U/Skh6yfBzF4mOvtzGzCbaHj4sdk+A2RmdxKdqX0S+Le7f5DNx69H/ROB/wNeIDr47Uh0Zu+OmOp3A1qkb8cxr4uZbQdcBPRnzddv0DcvIrIm5ahyNIu1EstS5ahyVCQpylHlaBZr5e1nUuVoblDHZwBmdjcwCHiH1XNDuLufEahe/7rud/evQtStUn9C3eV9l4C1TwPGu/vC1O2OwBHufmOomqk6sQw/N7NKVl8yUfXFGssZGDObBmzr7vNStzsDr7v7hoHr7k80x0gvostl+gGfuPvGIeuman9C9DedzOrXL+nfQcC62xOt1jfOzLoAbd29pjPIIjlPOVq9vHI0w1qJZalyVDkqkgTlaPXyytEMa+XdZ1LlaG7lqDo+AzCzj4Hhrl9ucGb2jruPrLZtqrtvnlCTcoqZPQ/s7dGKiVg0yfIT7r5b4LrvArsAz7n75ma2M9EbiJNC1k3VfsvdY1mtr0rNC4nm5tnQ3YeaWS/g/jguzxFpjJSj8VGOhqUcjYdyVGRNytH4KEfDSyJLlaO5laNa1T2MD4hWPvsuzqLV5mdoRjSpdKiV3arW/b+atrv7JSHrphSYmaVD3cwKiZ57UHk0/Pxb4C0ze5ho3zoAeNvMzgFw96sD1S1z93lmVmBmBe4+wcwuC1SruglmdgXwALAyvTHwJTIHAZsDU1K1ZplZ24D1RBo75SjK0RyhHE1RjorESjmKcjSHJJGlytEcylF1fGaRmT1K9EJsC3xkZm+z5o66f8j67r7GjmlmBxJNABxaSZWvWwD7Ah/HUBfgaeA/ZnYz0e/+FOCpGOreTg3Dz3PQ9NS/tIdT/4c+CC40szbAy8B4M/seKA9cMy19dm2L1P9GtG8Fu0QGKHV3N7P0G6bWAWuJNFrKUUA5mmuUo8pRkdgoRwHlaC5KIkuVozlEl7pnkZntWNf97v5SXG1JM7M33X3rmGs2Bx5x9z1jqFUAnAzsSnRAeAa4zd2DHvyTGH6eT1IH2hVEf9OjiOasGR9yXpP0GcNUTYjCZS7waui5TczsV8AQYHfgr8DxwL3ufn3IuiKNjXJ0VU3lqGREOaoclfykHF1VUzkqGVGO5laOquMzADO7zN3PW9e2AHUPrnKzgGiOhh3dfZuQdWtoR0fgbXcfEmfdOJjZqNSXPwUKiXf4uQSUmtekuk7AnsBF7v7vwPV3B/YgCrqn3f3ZkPVEGjPlqHJUmh7lqEjjoRxVjkrToxwNRx2fAZjZFHcfVW3be+4+InDdcVVulgNfAv9w9+8D132f1XO5FAJdgUvc/e8h66ZqDyE6IzGc6LIGINzcJpbgioH5oMq8QFXPckFMKwbW0qZORJNaj1rnN4tIVihHlaPSMMpREQHlKMpRaSDlaG7SHJ9ZZGanAmOBgWb2XpW72gKvha7v7seFrlGVmfVx92+I5lBJKwfmAHvH1IxxwIXA34CdgeNYfZDKOnffOdRjy9rzAjUG7j7fzILsU9UmgK+pduzBKpIk5SigHJUMKEfXqq0clbyiHAWUo5IB5ehatXMiRzXiM4vMrD3QkeiMz2+r3LXE3efHUP9O4Ex3X5i63RG4yt2PD1RvGrCnu39ZbftxwAXuPihE3Wq1Jrv7Fmb2vrtvmtr2irv/KHDdzkQBtz3RgeJVorOKweb8iJOZXU/dB8AzAtVtQTQh+GDgPeAOd49rEuna2rQL0f4c7OypmV0CzAbuZvU8Mm3d/fJQNUUaI+Xoqu3K0SZOObpGm5SjIjFRjq7arhzNAUlkqXI0N3NUIz6zyN0XAYuAI8xsMyB9sHsFCB40wIh0yKTas8DMNg9Y72zgWTPbx90/AzCz84EjgTon1s6iFakJpT8zs9OBb4FuMdT9N9EKb4ekbh8F3AfsFkPtOExK/b8d0WUb96Vu/4Ro5cBQ7gTKiF4z+wAbA2cGrLdKtUtk0joBs4BjApffs9rk5DeZ2VtATgSNSH0pR5WjKEczpRyNKEclLylHlaPkTo5CMlmqHI3kVI5qxGcAZnYGcBLRRMMABwG3euAVsczsXWAnd1+Qut0JeCl95ilQzV2BW4ADgROBMcC+6TaEZmZjgI+BDsAfiVZbu9zd3wxcd7K7b1Ft2yR3Hx2ybtxSc8js4e5lqdvFwDOhLrGodqa0iGhS8ljmMjGz/tU2OTDP3UtiqP06cAPRGxgHjgBOc/dtQ9cWaYyUo8rRXKEcVY6KJEE5qhzNJXFmqXI0N3NUIz7DOBHYKr2DmtllwBtA0KABrgJeN7P/pm7/BPhzyILu/ryZHQu8CLwO7OruK0LWrFZ/YurLpUTzqcRlgpkdDvwndftQ4PEY68elF9GcQOkzxG1S20IpS3/h7uWBpjKpkbt/FVuxtR0JXJv6B9GlKkcm1xyRxClHY6IcDU45Gg/lqMialKMxUY7GIs4sVY5GcipHNeIzgNQQ5THpA25qnoiJIc90Vak9HNiFaF6G5939o4C1qq541pzoIFFBDCuemdkjdd3v7vuHqp2qvwRoDVSmNhUA6TMxQZ97nFLz41wEpFcP3BG4yN3vDFSvgtW/RwNaAstIcBU9EYmfclQ5mivHe+WoiCRBOaoczaXjfZxZqhzNTer4DMDMzgF+DjyY2nQg8E93vyZQvXbuvjh1KcFa4pjIOm5mNhf4GvgX8BasuXKeu7+URLtykZn1ANLzfbzl7rOTbE+uM7MpcV1OIdJYKUfDU47GRzkaL+WoiHI0DsrReClL45OLOaqOz0DMbBTRCmsGvOzuUwPWeszd9zWzL1hzMtz0WYmBoWonxcwKgd2J5p4YQTSs/1/u/mGMbdgf2CF180V3fyyu2qGZ2TB3/yS1H6/F3afE3aZ8YWZT3T3kJPAiTYJyNCzlaFjK0eQoR0UiytGwlKPhKUuTkYs5qo7PLKrtDFdaLp7pagzMrDlR4FwBXBJ60u5UzUuJJs4en9p0BDDZ3X8bunYczOxWdz8pNZF0de7uu8TeqDxhZn9y9wuSbodIEpSjyVCOZp9yNDnKUclnytFkKEfDUJYmIxdzVB2fWVTlDJcB/YAFqa87ADPdfYNAdeschpyrZ0JSAfNjooP8AOAR4A53/zaG2u8BI929MnW7EJjq7iNC1xYRyVXK0XgpR0VEcotyNF7KUZGmQau6Z1E6SMzsZuARd38idXtvYLeApa+qq1lEk0vnFDO7E9gEeBK42N0/SKAZHVi9slz7BOoHZ2bFwKlUuYQCuMXdy2r9IVlvZrY10SqbGwHNgEKgRJNnS75RjsZHORoP5Wg8lKMiEeVofJSj8VGWhpfrOaoRnwGY2WR336LatknuPjqpNuUaM6ukyqp1Ve8ihtXWzOxw4DKileWM6CB8vrv/O2TduJnZbUAxkF4x72igwt1PTK5VucfMJgGHA/cDo4FjgMHu/vtEGyaSEOVoeMrReChH46EcFVmTcjQ85Wh8lKXh5XqOasRnGD+Y2QXAPUQHwZ8B80IVM7OD67rf3R8IVTsp7l6QVG0zKwAqga2J5lUx4LwcXVlujLtvVuX2C2b2bmKtyWHu/rmZFbp7BTDOzF5Puk0iCVKOBqYcjY1yNCbKUZE1KEcDU47GSlkag1zOUXV8hnEEcCHwYOr2y6ltoexXx30O5FzQJMndK83sdHf/D9E8LrmswswGuft0ADMbCFQk3KZctMzMmgHvmNnlwHdA64TbJJIk5WgOU44qRwNQjoqsSTmaw/IsR0FZGoeczlFd6i7SAGb2B2A5cB+rL3HIuZUSzWxXYBwwg+hMYn/gOHevaWU9aSAz6w98T3QJx9lEc/Tc6O6fJ9owEZFAlKPK0WxSjopIvsmXHAVlaRxyPUfV8RmAmQ0FfkW0stuqUbXuHnRSZzNrT3RmLz3p70vAJe6+KGTdfJRaMbE6d/eBsTcmsNRqhRsShcwn7r4y4SaJSI5TjuY+5aiISDjK0dyXTzkKylLJjDo+A0jNN3EzMJkqQ7DdfXLguv8DPmDNSX83c/c651wRqS4f5+lJkpntC/yR6OxlETFNii7SWClHpalTjsZLOSqyJuWo5AJlaXxyPUfV8RlATavoxVT3HXcfua5tkjkzOw0Y7+4LU7c7Ake4+42JNixLzGxcHXe7ux8fW2PygJl9DhwMvO86KIsoR/OAclQ5mk3KUZE1KUdzX67nKChL45TrOaqOzwDM7CKi+REeBFYNwQ4934aZvQH82t1fTd3eDrjS3bcJWTcf1RLqU91984SaJE2YmU0AdnX3yqTbItIYKEdzn3JUskk5KrIm5WjuU45KNuV6jqrjM4Ck5tsws82Au4gmogVYAPzc3d8LWTcfmdl7RJdteOp2IfCeu2+cbMuyy8y6A38Bern73mY2HNjG3W9PuGk5xczGEF1a8BJrvjm9OrFGiSRIOZr7lKPK0WxSjoqsSTma+/IlR0FZGodcz1F1fOYAM+vn7jOr3G4H4O6Lk2tVbjOzK4gmC78ZcOAU4Gt3PzfJdmWbmT1JtILe7919MzMrAqa6+6YJNy2nmNkzwFLgfWDVWTZ3vzixRonkEeVo/JSjytFsUo6KJEs5Gr98yVFQlsYh13NUHZ9ZZGa/cffLU1//xN3vr3LfX9z9d4HqTnH3Uamv/+fuh4SoI6uZWQFwMrAr0cS/zwC3uXtFnT/YxJjZRHcfU/WyCc3Tk31mNsndRyfdDpGkKUfzh3JUOZpNylGRiHI0f+RLjoKyNA65nqMFSTcgxxxe5evzq923V8C6VuXroJcvSMTdK939Jnc/1N0PcfdbcjFkgBIz60x0FhEz2xpYlGyTctJzZrZH0o0QaQSUo3lCOSpZphwViShH80Qe5SgoS+OQ0zlalHQDcozV8nVNt7PJa/laAjGzIcBfgeFAi/T20PPmJOBc4BFgkJm9BnQFDk22STnpNOA3ZrYSKCM6Xri7t0u2WSKxU47mCeWoZJlyVCSiHM0TeZSjoCyNQ07nqDo+s6uuA37IANjMzBYT7ZwtU19Dju2sjcw44ELgb8DOwHGEfTORCHefbGY7AhsSPb9p7l6WcLNyjru3TboNIo2EcjR/KEcla5SjIqsoR/NHXuQoKEvjkOs5qjk+s8jMKoASUgd8YFn6LqCFuxcn1TbJLjOb7O5bmNn76UmVzewVd/9R0m3LJjObBNwB/MvdFyTdnlxmZiOIJihfdULK3R9IrEEiCVCO5g/lqGSbclREOZpP8iVHQVkal1zOUY34zCJ3L0y6DRKbFakJpT8zs9OBb4FuCbcphMOJzh5OTAXOOOAZ1xmTrDKzO4ARwIesXkXPgZwIGpH6Uo7mFeWoZI1yVCSiHM0r+ZKjoCwNLtdzVCM+RRrAzMYAHwMdgD8C7YHL3f3NJNsVSipU9wVuIjoQ3gFc6+7zE21YjjCzj9x9eNLtEBGJi3JUOZpNylERyTf5lqOgLA0p13NUHZ8iUqfUkPfjgH2Ap4HxwPbA0e4+MsGm5Qwzux24yt0/SrotIiKSXcrR8JSjIiK5TVkaVq7nqC51F1kPZvZIXfe7+/5xtSUOZjYZWAjcDvzW3Vem7nrLzLZLrGG5507gDTObDaxk9UTwI5JtlohIdilHlaOBKEdFJC/kW46CsjQmOZ2jGvEpsh7MbC7wNfAv4C2qrZzn7i8l0a5QzGygu89Iuh25zsw+B84B3mf1nCq4+1eJNUpEJADlqISgHBWRfJFvOQrK0jjkeo6q41NkPZhZIbA7cATR5L+PE60u92GiDQvEzDoAx7D26m5nJNSknGRmL7j7Lkm3Q0QkNOVoRDmaXcpREckX+ZajoCyNQ67nqDo+RRrIzJoTBc4VwCXufn3CTco6M3sdeJO1z/zcmVijcpCZ3Ug0MfmjRJcWAODuObGKnohITZSjki3KURHJR/mQo6AsjUOu56jm+BRZT6mA+TFRyAwArgNy4oBQgxbufk7SjcgDLYkCZo8q25zc3a9EJI8pRyUA5aiI5I08y1FQlsYhp3NUIz5F1oOZ3QlsAjwJ/NvdP0i4SUGZ2dnAUuAx1jzzMz+xRomISJOlHI0oR0VEpCHyLUdBWSqZU8enyHows0qgJHWz6osnvepZu/hbFY6ZnQb8mWgVvfTzdXcfmFijcpCZtQBOADYGWqS3u/vxiTVKRCQA5SigHM065aiI5It8y1FQlsYh13NUl7qLrAd3L0i6DTE7Bxjs7j8k3ZAcdzfwCbAncAlwFPBxoi0SEQlAOSqBKEdFJC/kYY6CsjQOOZ2j+fiiEZH6+xBYlnQj8sBgd/8DUJKapPvHwKYJt0lERDKnHI2HclREJHcpS8PL6RzViE8RqUsF8I6ZTWDN+VTOSK5JOaks9f9CM9sEmE00UbmIiDRtytF4KEdFRHKXsjS8nM5RdXyKSF0eSv2TsG41s47AH4BHgDbA/yXbJBERyYKHUI7GQTkqIpK7HkJZGlpO56gWNxKRejGzUe4+Jel2iIiINEXKURERkcwoS6Uh1PEpIvViZlPcfVTS7chFZnZODZsXAZPd/Z2YmyMiIgEoR8NRjoqI5AdlaRi5nqNa3EhE6suSbkAOGw2cAvRO/TsJ2An4h5n9JsF2iYhI9ihHw1GOiojkB2VpGDmdoxrxKSL1YmYHuvtDSbcjF5nZ08Ah7r40dbsN8F/gIKKzbMOTbJ+IiGROORqOclREJD8oS8PI9RzV4kYishYzq+nygZnp7ZpXJev6AaVVbpcB/d19uZmtrOVnRESkkVKOxk45KiKSY5SlscrpHFXHp4jU5Ko67nNgl7gakifuBd40s4dTt/cD/mVmrYGPkmuWiIg0kHI0XspREZHcoyyNT07nqC51FxFpBMxsC2B7onlrXnX3SQk3SUREpMlQjoqIiDRcLueoOj5FZC1mtou7v2BmB9d0v7s/EHebREREmgrlqIiISGaUpZItutRdRGqyI/AC0RD36hxQyIiIiNROOSoiIpIZZalkhUZ8ioiIiIiIiIiISM7RiE8RqZWZNQcOAQZQ5Xjh7pck1SYREZGmQjkqIiKSGWWpZEodnyJSl4eBRcBkYGXCbREREWlqlKMiIiKZUZZKRnSpu4jUysw+cPdNkm6HiIhIU6QcFRERyYyyVDJVkHQDRKRRe93MNk26ESIiIk2UclRERCQzylLJiEZ8ishazOx9opXyioAhwAyiywoMcHcfkWDzREREGjXlqIiISGaUpZIt6vgUkbWYWf+67nf3r+Jqi4iISFOjHBUREcmMslSyRR2fIlInMxsFbE90tu01d5+ScJNERESaDOWoiIhIZpSlkgnN8SkitTKz/wPuBDoDXYBxZnZBsq0SERFpGpSjIiIimVGWSqY04lNEamVmHwObu/uK1O2WwBR33yjZlomIiDR+ylEREZHMKEslUxrxKSJ1+RJoUeV2c2B6Mk0RERFpcr5EOSoiIpKJL1GWSgY04lNEamVmDwFjgGeJ5lPZHXgV+B7A3c9IrHEiIiKNnHJUREQkM8pSyZQ6PkWkVmb287rud/c742qLiIhIU6McFRERyYyyVDKljk8RqZOZNQOGpm5Oc/eyJNsjIiLSlChHRUREMqMslUyo41NEamVmOxGtoPclYEBf4Ofu/nJyrRIREWkalKMiIiKZUZZKptTxKSK1MrPJwJHuPi11eyjwL3ffItmWiYiINH7KURERkcwoSyVTWtVdROpSnA4YAHf/FChOsD0iIiJNiXJUREQkM8pSyUhR0g0QkUZtspndDtydun0UMDnB9oiIiDQlylEREZHMKEslI7rUXURqZWbNgdOA7YnmU3kZuNHdVybaMBERkSZAOSoiIpIZZalkSh2fIlIjMysA3nP3TZJui4iISFOjHBUREcmMslSyQXN8ikiN3L0SeNfM+iXdFhERkaZGOSoiIpIZZalkg+b4FJG69AQ+NLO3gZL0RnffP7kmiYiINBnKURERkcwoSyUj6vgUkbpcnHQDREREmjDlqIiISGaUpZIRdXyKSF32cffzqm4ws8uAlxJqj4iISFOiHBUREcmMslQyojk+RaQuu9ewbe/YWyEiItI0KUdFREQyoyyVjGjEp4isxcxOBcYCg8zsvSp3tQVeT6ZVIiIiTYNyVEREJDPKUskWc/ek2yAijYyZtQc6An8FflvlriXuPj+ZVomIiDQNylEREZHMKEslW9TxKSK1MrN+NW1395lxt0VERKSpUY6KiIhkRlkqmVLHp4jUyszeBxwwoAWwATDN3TdOtGEiIiJNgHJUREQkM8pSyZTm+BSRWrn7plVvm9ko4OSEmiMiItKkKEdFREQyoyyVTGlVdxGpN3efAoxJuh0iIiJNkXJUREQkM8pSWV8a8SkitTKzc6rcLABGAXMTao6IiEiTohwVERHJjLJUMqWOTxGpS9sqX5cDjwP/S6gtIiIiTY1yVEREJDPKUsmIFjcSkXUys9buXpJ0O0RERJoi5aiIiEhmlKXSUJrjU0RqZWbbmNlHwMep25uZ2Y0JN0tERKRJUI6KiIhkRlkqmVLHp4jU5RpgT2AegLu/C+yQZINERESakGtQjoqIiGTiGpSlkgF1fIpIndz962qbKhJpiIiISBOkHBUREcmMslQyocWNRKQuX5vZtoCbWTPgDFKXGIiIiMg6KUdFREQyoyyVjGhxIxGplZl1Aa4FdgMMeAY4093nJdowERGRJkA5KiIikhllqWRKHZ8iIiIiIiIiIiKSc3Spu4isxcz+r4673d3/GFtjREREmhjlqIiISGaUpZItGvEpImsxs3Nr2NwaOAHo7O5tYm6SiIhIk6EcFRERyYyyVLJFHZ8iUiczawucSRQw/wGucvfvk22ViIhI06AcFRERyYyyVDKhS91FpEZm1gk4BzgKuBMY5e4Lkm2ViIhI06AcFRERyYyyVLJBHZ8ishYzuwI4GLgV2NTdlybcJBERkSZDOSoiIpIZZalkiy51F5G1mFklsBIoB6oeJIxoIul2iTRMRESkCVCOioiIZEZZKtmijk8RERERERERERHJOQVJN0BEREREREREREQk29TxKSIiIiIiIiIiIjlHHZ8iIiIiIiIiIiKSc9TxKSIiIiIiIiIiIjlHHZ8iIiIiIiIiIiKSc9TxKSIiIiIiIiIiIjnn/wES4RR7A+CvXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1614.38x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check expression of the three genes in the query set.\n",
    "# Here we use `majority_voting` from CellTypist as the cell type labels for this dataset.\n",
    "sc.pl.violin(adata_500, top_3_genes, groupby = 'majority_voting', rotation = 90)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
